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docgen/src/main.rs
|
use regex::Regex;
use std::fs;
use std::path::Path;
fn main() {
// TENSOR DOC
let trait_path = "src/operators/tensor/core.cairo";
let doc_path = "docs/framework/operators/tensor";
let label = "tensor";
let trait_name = "TensorTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// NN DOC
let trait_path = "src/operators/nn/core.cairo";
let doc_path = "docs/framework/operators/neural-network";
let label = "nn";
let trait_name = "NNTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// SEQUENCE DOC
let trait_path = "src/operators/sequence/core.cairo";
let doc_path = "docs/framework/operators/sequence";
let label = "sequence";
let trait_name = "SequenceTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// FIXED POINT DOC
let trait_path = "src/numbers/fixed_point/core.cairo";
let doc_path = "docs/framework/numbers/fixed-point";
let label = "fp";
let trait_name = "FixedTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// COMPLEX NUMBER DOC
let trait_path = "src/numbers/complex_number/complex_trait.cairo";
let doc_path = "docs/framework/numbers/complex-number";
let label = "complex";
let trait_name: &str = "ComplexTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// TREE ENSEMBLE CLASSIFIER DOC
let trait_path = "src/operators/ml/tree_ensemble/tree_ensemble_classifier.cairo";
let doc_path = "docs/framework/operators/machine-learning/tree-ensemble-classifier";
let label = "tree_ensemble_classifier";
let trait_name: &str = "TreeEnsembleClassifierTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// TREE ENSEMBLE REGRESSOR DOC
let trait_path = "src/operators/ml/tree_ensemble/tree_ensemble_regressor.cairo";
let doc_path = "docs/framework/operators/machine-learning/tree-ensemble-regressor";
let label = "tree_ensemble_regressor";
let trait_name: &str = "TreeEnsembleRegressorTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// LINEAR REGRESSOR DOC
let trait_path = "src/operators/ml/linear/linear_regressor.cairo";
let doc_path = "docs/framework/operators/machine-learning/linear-regressor";
let label = "linear_regressor";
let trait_name: &str = "LinearRegressorTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// LINEAR CLASSIFIER DOC
let trait_path = "src/operators/ml/linear/linear_classifier.cairo";
let doc_path = "docs/framework/operators/machine-learning/linear-classifier";
let label = "linear_classifier";
let trait_name: &str = "LinearClassifierTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// SVM REGRESSOR DOC
let trait_path = "src/operators/ml/svm/svm_regressor.cairo";
let doc_path = "docs/framework/operators/machine-learning/svm-regressor";
let label = "svm_regressor";
let trait_name: &str = "SVMRegressorTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// SVM CLASSIFIER DOC
let trait_path = "src/operators/ml/svm/svm_classifier.cairo";
let doc_path = "docs/framework/operators/machine-learning/svm-classifier";
let label = "svm_classifier";
let trait_name: &str = "SVMClassifierTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
// NORMALIZER DOC
let trait_path = "src/operators/ml/normalizer/normalizer.cairo";
let doc_path = "docs/framework/operators/machine-learning/normalizer";
let label = "normalizer";
let trait_name: &str = "NormalizerTrait";
doc_trait(trait_path, doc_path, label);
doc_functions(trait_path, doc_path, trait_name, label);
}
fn doc_trait(trait_path: &str, doc_path: &str, label: &str) {
// Open and read core.cairo file
let path_str = format!("../{}", trait_path);
let path = Path::new(&path_str);
let contents = fs::read_to_string(&path).expect("Could not read the file");
// Create a regular expression to match the comment lines
let re = Regex::new(r#"/// (\w+) - (.*)"#).unwrap();
// Initialize an empty string to store our new formatted table
let mut table = String::from("| function | description |\n| --- | --- |\n");
// Go through the file and look for comments with our specific format
for cap in re.captures_iter(&contents) {
// Check if the function is the Trait definition and skip it
if &cap[1] == "Trait" {
continue;
}
// Add the function name and description to our table
let func_name = format!(
"[`{}.{}`]({}.{}.md)",
label,
&cap[1],
label,
&cap[1].replace('_', r"\_")
);
let func_desc = &cap[2];
table += &format!("| {} | {} |\n", func_name, func_desc);
}
// Open the README.md file
let readme_path_str = format!("../{}/README.md", doc_path);
let readme_path = Path::new(&readme_path_str);
let readme = fs::read_to_string(&readme_path).expect("Could not read the file");
// Use regex to replace the table
let re_table = Regex::new(r"(?ms)\n\n\| fun.*?(\n[^|]|\z)").unwrap();
let new_readme = re_table.replace(&readme, &("\n\n".to_owned() + &table + "\n"));
// Write the updated contents back to README.md
fs::write(&readme_path, &*new_readme).expect("Could not write the file");
}
fn doc_functions(trait_path: &str, doc_path: &str, trait_name: &str, label: &str) {
let filepath_str = format!("../{}", trait_path);
let filepath = Path::new(&filepath_str);
let contents = fs::read_to_string(filepath).expect("Something went wrong reading the file");
// Find the trait block
let trait_re = Regex::new(&format!(
r"(?s)trait\s+{}\s*(<[\w\s,]*>)?\s*\{{.*?\n\s*\}}",
trait_name
))
.unwrap();
let trait_match = trait_re.captures(&contents).unwrap();
let trait_block = trait_match.get(0).unwrap().as_str();
// Iterate over each function
let func_re = Regex::new(r"(?s)(///.*?\n)\s*fn (\w+)\((.*?)\) -> (.*?);").unwrap();
for func_match in func_re.captures_iter(trait_block) {
let func_name = func_match.get(2).unwrap().as_str();
let doc_comment = func_match.get(1).unwrap().as_str();
// Go to the appropriate markdown file and write the transformed doc comment
let markdown_filename = format!("../{}/{}.{}.md", doc_path, label, func_name);
let transformed_comment = doc_comment
.lines()
.map(|line| {
line.trim_start().strip_prefix("/// ").unwrap_or(
line.trim_start()
.strip_prefix("///")
.unwrap_or(line.trim_start()),
)
})
.collect::<Vec<_>>()
.join("\n");
// Write or replace the transformed comment into the markdown file
fs::write(markdown_filename, transformed_comment).expect("Unable to write file");
}
}
|
https://github.com/gizatechxyz/orion
|
nodegen/__init__.py
|
https://github.com/gizatechxyz/orion
|
|
nodegen/file_manager.py
|
import os
from pathlib import Path
BASE_PATH = "./tests/nodes"
class ModFile:
def __init__(self):
"""
Initialize a ModFile object.
This method creates a new file with a .cairo extension in the BASE_PATH directory.
If the directory doesn't exist, it's created. The contents of the file are then read
into the buffer attribute.
"""
self.path = Path(f"{BASE_PATH}.cairo")
self.path.parent.mkdir(parents=True, exist_ok=True)
with self.path.open("r") as f:
self.buffer = f.readlines()
def update(self, name: str):
"""
Update the .cairo file with a new module statement.
Args:
name (str): The name of the module to be added.
This method checks if a module statement for the given name already exists in the buffer.
If it doesn't, the new module statement is appended to the file.
"""
statement = f"mod {name};"
if any([line.startswith(statement) for line in self.buffer]):
return
with self.path.open("a") as f:
f.write(f"{statement}\n")
class File:
def __init__(self, path: str):
"""
Initialize a File object.
Args:
path (str): The file path where the File object will operate.
This method creates a new file at the specified path. If the file already exists, its
contents are read into the buffer attribute.
"""
self.path = Path(path)
self.path.parent.mkdir(parents=True, exist_ok=True)
self.buffer = []
if os.path.isfile(path):
with self.path.open("r") as f:
self.buffer = f.readlines()
def dump(self):
"""
Write the contents of the buffer to the file.
This method writes each line in the buffer to the file, ensuring each line is
properly terminated with a newline character.
"""
with self.path.open("w") as f:
f.writelines([f"{line}\n" for line in self.buffer])
class CairoTest(File):
def __init__(self, file: str):
super().__init__(os.path.join(BASE_PATH, file))
@classmethod
def base_template(
cls, name: str, arg_cnt: int, refs: list[str], func_sig: str, out_cnt: int = 1
) -> list[str]:
"""
Create a template for a Cairo test function which expects a tensor output.
Args:
name (str): Name of the test function.
arg_cnt (int): Number of arguments for the function.
refs (list[str]): List of references (modules) to be used in the function.
func_sig (str): The function signature.
out_cnt (int): Number of outputs for the function. Defaults to 1.
Returns:
list[str]: A list of strings that together form the template of a Cairo test function.
This method generates a list of strings that form the template of a Cairo test function,
including module imports, function definition, and assertions.
"""
template = [
*[f"mod input_{i};" for i in range(arg_cnt)],
*[f"mod output_{i};" for i in range(out_cnt)],
"",
"",
*[f"use {ref};" for ref in refs],
"",
"#[test]",
"#[available_gas(2000000000)]",
f"fn test_{name}()" + " {",
*[f" let input_{i} = input_{i}::input_{i}();" for i in range(arg_cnt)],
*[f" let z_{i} = output_{i}::output_{i}();" for i in range(out_cnt)],
""
]
# Handling conditional function signature based on the number of outputs
if out_cnt > 1:
template.append(f" let ({', '.join(f'y_{i}' for i in range(out_cnt))}) = {func_sig};")
else:
template.append(f" let y_0 = {func_sig};")
# Continue appending to the template
template.extend([
"",
*[f" assert_eq(y_{i}, z_{i});" for i in range(out_cnt)],
"}"
])
return template
@classmethod
def sequence_template(cls, name: str, arg_cnt: int, refs: list[str], func_sig: str) -> list[str]:
"""
Create a template for a Cairo test function which expects a tensor sequence.
Args:
name (str): Name of the test function.
arg_cnt (int): Number of arguments for the function.
refs (list[str]): List of references (modules) to be used in the function.
func_sig (str): The function signature.
Returns:
list[str]: A list of strings that together form the template of a Cairo test function.
This method generates a list of strings that form the template of a Cairo test function,
including module imports, function definition, and assertions.
"""
return [
*[f"mod input_{i};" for i in range(arg_cnt)],
*[ "mod output_0;"],
*[ ""],
*[ ""],
*[f"use {ref};" for ref in refs],
*[ ""],
*[ "#[test]"],
*[ "#[available_gas(2000000000)]"],
*[f"fn test_{name}()"+" {"],
*[f" let input_{i} = input_{i}::input_{i}();" for i in range(arg_cnt)],
*[ " let z = output_0::output_0();"],
*[ ""],
*[f" let y = {func_sig};"],
*[ ""],
*[ " assert_seq_eq(y, z);"],
*[ "}"],
]
class CairoData(File):
def __init__(self, file: str):
super().__init__(os.path.join(BASE_PATH, file))
@classmethod
def base_template(
cls, func: str, dtype: str, refs: list[str], data: list[str], shape: tuple
) -> list[str]:
"""
Create a base template for data representation in Cairo.
Args:
func (str): The function name.
dtype (str): The data type of the tensor.
refs (list[str]): A list of module references.
data (list[str]): The data to be included in the tensor.
shape (tuple): The shape of the tensor.
Returns:
list[str]: A list of strings that together form the template of a data function in Cairo.
This method generates a list of strings representing a function in Cairo for data handling,
defining the shape and contents of a tensor.
"""
template = [
*[f"use {ref};" for ref in refs],
*[""],
*[f"fn {func}() -> Tensor<{dtype}>" + " {"],
*[" let mut shape = ArrayTrait::<usize>::new();"],
*[f" shape.append({s});" for s in shape],
*[""],
*[" let mut data = ArrayTrait::new();"],
*[f" data.append({d});" for d in data],
*[" TensorTrait::new(shape.span(), data.span())"],
*["}"],
]
return template
@classmethod
def sequence_template(
cls,
func: str,
dtype: str,
refs: list[str],
data: list[list[str]],
shape: list[tuple],
) -> list[str]:
"""
Create a template for handling tensor sequences in Cairo.
Args:
func (str): The function name.
dtype (str): The data type of the tensor sequence.
refs (list[str]): A list of module references.
data (list[list[str]]): The data to be included in each tensor.
shape (list[tuple]): The shapes of each tensor in the sequence.
Returns:
list[str]: A list of strings that together form the template of a sequence tensor function in Cairo.
This method generates a list of strings representing a function in Cairo for handling a sequence
of tensors, each with its own data and shape.
"""
def expand_sequence_init(s: list[tuple], d: list[list[str]]) -> list[str]:
snippet = []
for i in range(len(s)):
snippet += [
*[" let mut shape = ArrayTrait::<usize>::new();"],
*[f" shape.append({s});" for s in s[i]],
*[""],
*[" let mut data = ArrayTrait::new();"],
*[f" data.append({d});" for d in d[i]],
*[""],
*[
" sequence.append(TensorTrait::new(shape.span(), data.span()));"
],
*[""],
]
return snippet
template = [
*[f"use {ref};" for ref in refs],
*[""],
*[f"fn {func}() -> Array<Tensor<{dtype}>>" + " {"],
*[" let mut sequence = ArrayTrait::new();"],
*[""],
*expand_sequence_init(shape, data),
*[" sequence"],
*["}"],
]
return template
|
https://github.com/gizatechxyz/orion
|
nodegen/helpers.py
|
from enum import Enum
import os
from typing import List
from .file_manager import CairoTest, CairoData, ModFile
import numpy as np
class FixedImpl(Enum):
FP8x23 = 'FP8x23'
FP16x16 = 'FP16x16'
FP32x32 = 'FP32x32'
def to_fp(x: np.ndarray, fp_impl: FixedImpl):
match fp_impl:
case FixedImpl.FP8x23:
return (x * 2**23).astype(np.int64)
case FixedImpl.FP16x16:
return (x * 2**16).astype(np.int64)
case FixedImpl.FP32x32:
return (x * 2**32).astype(np.int64)
class Dtype(Enum):
FP8x23 = 'FP8x23'
FP16x16 = 'FP16x16'
FP32x32 = 'FP32x32'
I8 = 'i8'
I32 = 'i32'
U32 = 'u32'
BOOL = 'bool'
COMPLEX64 = 'complex64'
class Tensor:
def __init__(self, dtype: Dtype, shape: tuple, data: np.ndarray):
self.dtype = dtype
self.shape = shape
self.data = data
Sequence = List[Tensor]
class Trait(Enum):
TENSOR = 'TENSOR'
NN = 'NN'
SEQUENCE = 'SEQUENCE'
def make_test(inputs: list[Tensor | Sequence], output: Tensor | Sequence, func_sig: str, name: str, trait: Trait = Trait.TENSOR):
"""
Generate and write Cairo tests based on the provided inputs and output.
Args:
inputs (list[Tensor | list[Tensor]]): A list of input tensors or tensor sequences.
output (Tensor | list[Tensor]): The expected output tensor or tensor sequences.
func_sig (str): The signature of the function to be tested.
name (str): The name of the test.
trait (Trait, optional): The trait of the tensors. Defaults to Trait.TENSOR.
"""
ModFile().update(name)
for i, input in enumerate(inputs):
input_data = CairoData(os.path.join(name, f"input_{i}.cairo"))
match input:
case list():
input_data.buffer = CairoData.sequence_template(
func=f"input_{i}",
dtype=input[0].dtype.value,
refs=get_data_refs(input[0].dtype),
data=get_data_statement_for_sequences(
input, input[0].dtype),
shape=[x.shape for x in input],
)
case Tensor():
input_data.buffer = CairoData.base_template(
func=f"input_{i}",
dtype=input.dtype.value,
refs=get_data_refs(input.dtype),
data=get_data_statement(input.data, input.dtype),
shape=input.shape,
)
input_data.dump()
match output:
case list():
output_data = CairoData(os.path.join(name, "output_0.cairo"))
output_data.buffer = CairoData.sequence_template(
func="output_0",
dtype=output[0].dtype.value,
refs=get_data_refs(output[0].dtype),
data=get_data_statement_for_sequences(output, output[0].dtype),
shape=[x.shape for x in output],
)
output_data.dump()
case tuple():
for i, out in enumerate(output):
output_data = CairoData(
os.path.join(name, f"output_{i}.cairo"))
output_data.buffer = CairoData.base_template(
func=f"output_{i}",
dtype=out.dtype.value,
refs=get_data_refs(out.dtype),
data=get_data_statement(out.data, out.dtype),
shape=out.shape,
)
output_data.dump()
case Tensor():
output_data = CairoData(os.path.join(name, "output_0.cairo"))
output_data.buffer = CairoData.base_template(
func="output_0",
dtype=output.dtype.value,
refs=get_data_refs(output.dtype),
data=get_data_statement(output.data, output.dtype),
shape=output.shape,
)
output_data.dump()
test_file = CairoTest(f"{name}.cairo")
match output:
case list():
test_file.buffer = CairoTest.sequence_template(
name=name,
arg_cnt=len(inputs),
refs=get_all_test_refs(find_all_types([*inputs, *output]), trait),
func_sig=func_sig,
)
case Tensor():
test_file.buffer = CairoTest.base_template(
name=name,
arg_cnt=len(inputs),
refs=get_all_test_refs(find_all_types([*inputs, output]), trait),
func_sig=func_sig,
)
case tuple():
test_file.buffer = CairoTest.base_template(
name=name,
arg_cnt=len(inputs),
out_cnt=len(output),
refs=get_all_test_refs(find_all_types([*inputs, output]), trait),
func_sig=func_sig,
)
test_file.dump()
def get_data_refs(dtype: Dtype) -> list[str]:
refs = [
*trait_to_ref[Trait.TENSOR],
*dtype_to_tensor[dtype],
*dtype_to_numbers[dtype],
]
return refs
def get_data_statement(data: np.ndarray, dtype: Dtype) -> list[str]:
match dtype:
case Dtype.U32:
return [f"{int(x)}" for x in data.flatten()]
case Dtype.I32:
return [f"{int(x)}" for x in data.flatten()]
case Dtype.I8:
return [f"{int(x)}" for x in data.flatten()]
case Dtype.FP8x23:
return ["FP8x23 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"}" for x in data.flatten()]
case Dtype.FP16x16:
return ["FP16x16 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"}" for x in data.flatten()]
case Dtype.FP32x32:
return ["FP32x32 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"}" for x in data.flatten()]
case Dtype.BOOL:
return [str(x).lower() for x in data.flatten()]
case Dtype.COMPLEX64:
return ["complex64 { "+"real: FP64x64 { "+f"mag: {abs(int(np.real(x)))}, sign: {str(np.real(x) < 0).lower()} "+"} , img: FP64x64 { "+f"mag: {abs(int(np.imag(x)))}, sign: {str(np.imag(x) < 0).lower()} "+"} }" for x in data.flatten()]
def get_data_statement_for_sequences(data: Sequence, dtype: Dtype) -> list[list[str]]:
return [get_data_statement(x.data, dtype) for x in data]
def get_all_test_refs(dtypes: list[Dtype], trait: Trait) -> list[str]:
refs = []
for dtype in dtypes:
# refs += [*dtype_to_numbers[dtype]]
refs += get_test_refs(dtype, trait)
return list(set(refs))
def get_test_refs(dtype: Dtype, trait: Trait) -> list[str]:
if trait == Trait.NN and dtype == Dtype.BOOL:
raise Exception("NN trait does not support bool dtype")
if trait == Trait.NN:
dtype_ref = dtype_to_nn[dtype]
elif trait == Trait.SEQUENCE:
dtype_ref = dtype_to_sequence[dtype]
else:
dtype_ref = dtype_to_tensor[dtype]
refs = [
*trait_to_ref[trait],
*dtype_ref,
*dtype_to_partial_eq[dtype],
"orion::utils::{assert_eq, assert_seq_eq}",
]
return refs
def find_all_types(tensors: list[Tensor | Sequence]) -> list[Dtype]:
dtypes = []
for tensor in tensors:
if isinstance(tensor, list) or isinstance(tensor, tuple):
dtypes += [x.dtype for x in tensor]
else:
dtypes.append(tensor.dtype)
return list(set(dtypes))
trait_to_ref = {
Trait.TENSOR: [
"core::array::{ArrayTrait, SpanTrait}",
"orion::operators::tensor::{TensorTrait, Tensor}",
],
Trait.NN: [
"orion::numbers::FixedTrait",
"orion::operators::nn::NNTrait",
],
Trait.SEQUENCE: [
"core::array::{ArrayTrait, SpanTrait}",
"orion::operators::sequence::SequenceTrait",
],
}
dtype_to_tensor = {
Dtype.U32: ["orion::operators::tensor::{U32Tensor, U32TensorAdd}",],
Dtype.I32: ["orion::operators::tensor::{I32Tensor, I32TensorAdd}",],
Dtype.I8: ["orion::operators::tensor::{I8Tensor, I8TensorAdd}",],
Dtype.FP8x23: ["orion::operators::tensor::{FP8x23Tensor, FP8x23TensorAdd}",],
Dtype.FP16x16: ["orion::operators::tensor::{FP16x16Tensor, FP16x16TensorAdd}",],
Dtype.BOOL: ["orion::operators::tensor::BoolTensor",],
Dtype.COMPLEX64: ["orion::operators::tensor::Complex64Tensor",],
Dtype.FP32x32: ["orion::operators::tensor::FP32x32Tensor",],
}
dtype_to_nn = {
Dtype.U32: ["orion::operators::nn::U32NN",],
Dtype.I32: ["orion::operators::nn::I32NN",],
Dtype.I8: ["orion::operators::nn::I8NN",],
Dtype.FP8x23: ["orion::operators::nn::FP8x23NN",],
Dtype.FP16x16: ["orion::operators::nn::FP16x16NN",],
}
dtype_to_sequence = {
Dtype.U32: ["orion::operators::sequence::U32Sequence",],
Dtype.I32: ["orion::operators::sequence::I32Sequence",],
Dtype.I8: ["orion::operators::sequence::I8Sequence",],
Dtype.FP8x23: ["orion::operators::sequence::FP8x23Sequence",],
Dtype.FP16x16: ["orion::operators::sequence::FP16x16Sequence",],
}
dtype_to_partial_eq = {
Dtype.U32: ["orion::operators::tensor::U32TensorPartialEq",],
Dtype.I32: ["orion::operators::tensor::I32TensorPartialEq",],
Dtype.I8: ["orion::operators::tensor::I8TensorPartialEq",],
Dtype.FP8x23: ["orion::operators::tensor::FP8x23TensorPartialEq",],
Dtype.FP16x16: ["orion::operators::tensor::FP16x16TensorPartialEq",],
Dtype.FP32x32: ["orion::operators::tensor::FP32x32TensorPartialEq",],
Dtype.BOOL: ["orion::operators::tensor::BoolTensorPartialEq",],
Dtype.COMPLEX64: ["orion::operators::tensor::Complex64TensorPartialEq",],
}
dtype_to_numbers = {
Dtype.U32: ["orion::numbers::NumberTrait"],
Dtype.I32: ["orion::numbers::NumberTrait"],
Dtype.I8: ["orion::numbers::NumberTrait"],
Dtype.FP8x23: ["orion::numbers::{FixedTrait, FP8x23}",],
Dtype.FP16x16: ["orion::numbers::{FixedTrait, FP16x16}",],
Dtype.FP32x32: ["orion::numbers::{FixedTrait, FP32x32}",],
Dtype.BOOL: [],
Dtype.COMPLEX64: ["orion::numbers::{NumberTrait, complex64}",],
}
|
https://github.com/gizatechxyz/orion
|
nodegen/node/__init__.py
|
import argparse
import importlib
import os
import sys
class RunAll:
@classmethod
def run_all(cls):
for method_name in dir(cls):
if method_name.startswith('__') or method_name == 'run_all':
continue
method = getattr(cls, method_name)
if callable(method):
method()
# Add the path to the 'orion' directory to the Python path
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
def main():
parser = argparse.ArgumentParser(description="Generate nodes.")
parser.add_argument('node_class', help="The class of node to run.")
args = parser.parse_args()
class_name = args.node_class.capitalize()
# Verify that the specified Python file exists
filename = os.path.join('nodegen/node', args.node_class + '.py')
if not os.path.exists(filename):
print(f"Error: {filename} does not exist.")
return
# Import the module dynamically
module = importlib.import_module('nodegen.node.' + args.node_class)
# Get the class from the module
node_class = getattr(module, class_name)
# Instantiate the class and call the run_all method
node_instance = node_class()
node_instance.run_all()
if __name__ == "__main__":
main()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/abs.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Abs(RunAll):
@staticmethod
def abs_i32():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int32)
y = abs(x)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "abs_i32"
make_test([x], y, "input_0.abs()", name)
@staticmethod
def abs_i8():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int8)
y = abs(x)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "abs_i8"
make_test([x], y, "input_0.abs()", name)
@staticmethod
def abs_fp8x23():
x = to_fp(np.random.randint(-127, 127, (2, 2)
).astype(np.int64), FixedImpl.FP8x23)
y = abs(x)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "abs_fp8x23"
make_test([x], y, "input_0.abs()", name)
@staticmethod
def abs_fp16x16():
x = to_fp(np.random.randint(-127, 127, (2, 2)
).astype(np.int64), FixedImpl.FP16x16)
y = abs(x)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "abs_fp16x16"
make_test([x], y, "input_0.abs()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/acos.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Acos(RunAll):
@staticmethod
def acos_fp8x23():
x = np.random.uniform(-1, 1, (2, 2)).astype(np.float64)
y = np.arccos(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "acos_fp8x23"
make_test([x], y, "input_0.acos()", name)
@staticmethod
def acos_fp16x16():
x = np.random.uniform(-1, 1, (2, 2)).astype(np.float64)
y = np.arccos(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "acos_fp16x16"
make_test([x], y, "input_0.acos()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/acosh.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Acosh(RunAll):
@staticmethod
def acosh_fp8x23():
x = np.random.uniform(1, 5, (2, 2)).astype(np.float64)
y = np.arccosh(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "acosh_fp8x23"
make_test([x], y, "input_0.acosh()", name)
@staticmethod
def acosh_fp16x16():
x = np.random.uniform(1, 5, (2, 2)).astype(np.float64)
y = np.arccosh(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "acosh_fp16x16"
make_test([x], y, "input_0.acosh()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/add.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Add(RunAll):
@staticmethod
def add_u32():
def default():
x = np.random.randint(0, 3, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 3, (3, 3, 3)).astype(np.uint32)
z = x + y
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "add_u32"
make_test([x, y], z, "input_0 + input_1", name)
def broadcast():
x = np.random.randint(0, 3, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 3, (1, 3, 1)).astype(np.uint32)
z = x + y
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "add_u32_broadcast"
make_test([x, y], z, "input_0 + input_1", name)
default()
broadcast()
@staticmethod
def add_i32():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
z = x + y
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "add_i32"
make_test([x, y], z, "input_0 + input_1", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.int32)
z = x + y
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "add_i32_broadcast"
make_test([x, y], z, "input_0 + input_1", name)
default()
broadcast()
@staticmethod
def add_i8():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
z = x + y
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name = "add_i8"
make_test([x, y], z, "input_0 + input_1", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.int8)
z = x + y
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name = "add_i8_broadcast"
make_test([x, y], z, "input_0 + input_1", name)
default()
broadcast()
@staticmethod
def add_fp8x23():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = x + y
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "add_fp8x23"
make_test([x, y], z, "input_0 + input_1", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.float64)
z = x + y
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "add_fp8x23_broadcast"
make_test([x, y], z, "input_0 + input_1", name)
default()
broadcast()
@staticmethod
def add_fp16x16():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = x + y
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "add_fp16x16"
make_test([x, y], z, "input_0 + input_1", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.float64)
z = x + y
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "add_fp16x16_broadcast"
make_test([x, y], z, "input_0 + input_1", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/and.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class And(RunAll):
@staticmethod
def and_bool():
def default():
x = (np.random.randn(3, 4) > 0).astype(bool)
y = (np.random.randn(3, 4) > 0).astype(bool)
z = np.logical_and(x, y)
x = Tensor(Dtype.BOOL, x.shape, x.flatten())
y = Tensor(Dtype.BOOL, y.shape, y.flatten())
z = Tensor(Dtype.BOOL, z.shape, z.flatten())
name = "and_bool"
make_test([x, y], z, "BoolTensor::and(@input_0, @input_1)", name)
def broadcast():
x = (np.random.randn(3, 4, 5) > 0).astype(bool)
y = (np.random.randn(3, 4, 5) > 0).astype(bool)
z = np.logical_and(x, y)
x = Tensor(Dtype.BOOL, x.shape, x.flatten())
y = Tensor(Dtype.BOOL, y.shape, y.flatten())
z = Tensor(Dtype.BOOL, z.shape, z.flatten())
name = "and_bool_broadcast"
make_test([x, y], z, "BoolTensor::and(@input_0, @input_1)", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/argmax.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
def argmax_use_numpy(data: np.ndarray, axis: int = 0, keepdims: int = 1) -> np.ndarray:
result = np.argmax(data, axis=axis)
if keepdims == 1:
result = np.expand_dims(result, axis)
return result.astype(np.int64)
def argmax_use_numpy_select_last_index(
data: np.ndarray, axis: int = 0, keepdims: int = True
) -> np.ndarray:
data = np.flip(data, axis)
result = np.argmax(data, axis=axis)
result = data.shape[axis] - result - 1
if keepdims:
result = np.expand_dims(result, axis)
return result.astype(np.int64)
class Argmax(RunAll):
@staticmethod
def no_keepdims():
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 0
result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
x = Tensor(Dtype.FP16x16, data.shape, data.flatten())
y = Tensor(Dtype.I32, result.shape, result.flatten())
name = "argmax_no_keepdims"
make_test(
[x], y, "input_0.argmax(1, Option::Some(false), Option::None(()))", name)
@staticmethod
def keepdims():
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 1
result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
x = Tensor(Dtype.FP16x16, data.shape, data.flatten())
y = Tensor(Dtype.I32, result.shape, result.flatten())
name = "argmax_keepdims"
make_test(
[x], y, "input_0.argmax(1, Option::Some(true), Option::None(()))", name)
@staticmethod
def default_axes_keepdims():
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
keepdims = 1
result = argmax_use_numpy(data, keepdims=keepdims)
x = Tensor(Dtype.FP16x16, data.shape, data.flatten())
y = Tensor(Dtype.I32, result.shape, result.flatten())
name = "argmax_default_axes_keepdims"
make_test(
[x], y, "input_0.argmax(0, Option::Some(true), Option::None(()))", name)
@staticmethod
def negative_axis_keepdims():
data = np.array([[2, 1], [3, 10]], dtype=np.float32)
axis = -1
keepdims = 1
result = argmax_use_numpy(data, axis=axis, keepdims=keepdims)
x = Tensor(Dtype.FP16x16, data.shape, data.flatten())
y = Tensor(Dtype.I32, result.shape, result.flatten())
name = "argmax_negative_axis_keepdims"
make_test(
[x], y, "input_0.argmax(-1, Option::Some(true), Option::None(()))", name)
@staticmethod
def no_keepdims_select_last_index():
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 0
result = argmax_use_numpy_select_last_index(
data, axis=axis, keepdims=keepdims)
x = Tensor(Dtype.FP16x16, data.shape, data.flatten())
y = Tensor(Dtype.I32, result.shape, result.flatten())
name = "argmax_no_keepdims_select_last_index"
make_test(
[x], y, "input_0.argmax(1, Option::Some(false), Option::Some(true))", name)
@staticmethod
def keepdims_select_last_index():
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = 1
keepdims = 1
result = argmax_use_numpy_select_last_index(
data, axis=axis, keepdims=keepdims)
x = Tensor(Dtype.FP16x16, data.shape, data.flatten())
y = Tensor(Dtype.I32, result.shape, result.flatten())
name = "argmax_keepdims_select_last_index"
make_test(
[x], y, "input_0.argmax(1, Option::Some(true), Option::Some(true))", name)
@staticmethod
def default_axes_keepdims_select_last_index():
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
keepdims = 1
result = argmax_use_numpy_select_last_index(data, keepdims=keepdims)
x = Tensor(Dtype.FP16x16, data.shape, data.flatten())
y = Tensor(Dtype.I32, result.shape, result.flatten())
name = "argmax_default_axes_keepdims_select_last_index"
make_test(
[x], y, "input_0.argmax(0, Option::Some(true), Option::Some(true))", name)
@staticmethod
def negative_axis_keepdims_select_last_index():
data = np.array([[2, 2], [3, 10]], dtype=np.float32)
axis = -1
keepdims = 1
result = argmax_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims)
x = Tensor(Dtype.FP16x16, data.shape, data.flatten())
y = Tensor(Dtype.I32, result.shape, result.flatten())
name = "argmax_negative_axis_keepdims_select_last_index"
make_test(
[x], y, "input_0.argmax(-1, Option::Some(true), Option::Some(true))", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/argmin.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
def argmin_use_numpy(data: np.ndarray, axis: int = 0, keepdims: int = 1, dtype=np.int64) -> np.ndarray:
result = np.argmin(data, axis=axis)
if keepdims == 1:
result = np.expand_dims(result, axis)
return result.astype(dtype)
def argmin_use_numpy_select_last_index(
data: np.ndarray, axis: int = 0, keepdims: int = True, dtype=np.int64
) -> np.ndarray:
data = np.flip(data, axis)
result = np.argmin(data, axis=axis)
result = data.shape[axis] - result - 1
if keepdims:
result = np.expand_dims(result, axis)
return result.astype(dtype)
class Argmin(RunAll):
@staticmethod
def argmin_u32():
def argmin_1D():
def default_params():
x = np.random.randint(0, 255, (3)).astype(np.uint32)
y = argmin_use_numpy(x, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_u32_1D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = np.random.randint(0, 255, (3)).astype(np.uint32)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_u32_1D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = np.random.randint(0, 255, (3)).astype(np.uint32)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_u32_1D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_1D()
def argmin_2D():
def default_params():
x = np.random.randint(0, 255, (2, 2)).astype(np.uint32)
y = argmin_use_numpy(x, dtype=np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_u32_2D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = np.random.randint(0, 255, (2, 2)).astype(np.uint32)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_u32_2D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = np.random.randint(0, 255, (2, 2)).astype(np.uint32)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_u32_2D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_2D()
def argmin_3D():
def default_params():
x = np.random.randint(0, 255, (2, 2, 2)).astype(np.uint32)
y = argmin_use_numpy(x, dtype=np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_u32_3D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = np.random.randint(0, 255, (2, 2, 2)).astype(np.uint32)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_u32_3D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = np.random.randint(0, 255, (2, 2, 2)).astype(np.uint32)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_u32_3D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_3D()
@staticmethod
def argmin_i32():
def argmin_1D():
def default_params():
x = np.random.randint(-127, 127, (3)).astype(np.int32)
y = argmin_use_numpy(x, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i32_1D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = np.random.randint(-127, 127, (3)).astype(np.int32)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i32_1D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = np.random.randint(0, 255, (3)).astype(np.int32)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i32_1D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_1D()
def argmin_2D():
def default_params():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int32)
y = argmin_use_numpy(x, dtype=np.uint32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i32_2D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int32)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i32_2D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int32)
y = argmin_use_numpy_select_last_index(
x, dtype=np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i32_2D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_2D()
def argmin_3D():
def default_params():
x = np.random.randint(-127, 127, (2, 2, 2)).astype(np.int32)
y = argmin_use_numpy(x, dtype=np.uint32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i32_3D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = np.random.randint(-127, 127, (2, 2, 2)).astype(np.int32)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i32_3D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = np.random.randint(-127, 127, (2, 2, 2)).astype(np.int32)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i32_3D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_3D()
@staticmethod
def argmin_i8():
def argmin_1D():
def default_params():
x = np.random.randint(-127, 127, (3)).astype(np.int8)
y = argmin_use_numpy(x, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i8_1D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = np.random.randint(-127, 127, (3)).astype(np.int8)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i8_1D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = np.random.randint(0, 255, (3)).astype(np.int8)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i8_1D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_1D()
def argmin_2D():
def default_params():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int8)
y = argmin_use_numpy(x, dtype=np.uint32)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i8_2D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int8)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i8_2D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int8)
y = argmin_use_numpy_select_last_index(
x, dtype=np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i8_2D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_2D()
def argmin_3D():
def default_params():
x = np.random.randint(-127, 127, (2, 2, 2)).astype(np.int8)
y = argmin_use_numpy(x, dtype=np.uint32)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i8_3D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = np.random.randint(-127, 127, (2, 2, 2)).astype(np.int8)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i8_3D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = np.random.randint(-127, 127, (2, 2, 2)).astype(np.int8)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_i8_3D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_3D()
@staticmethod
def argmin_fp16x16():
def argmin_1D():
def default_params():
x = to_fp(np.random.randint(-127, 127, (3)
).astype(np.int8), FixedImpl.FP16x16)
y = argmin_use_numpy(x, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp16x16_1D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = to_fp(np.random.randint(-127, 127, (3)
).astype(np.int8), FixedImpl.FP16x16)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp16x16_1D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = to_fp(np.random.randint(0, 255, (3)).astype(
np.int8), FixedImpl.FP16x16)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp16x16_1D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_1D()
def argmin_2D():
def default_params():
x = to_fp(np.random.randint(-127, 127, (2, 2)
).astype(np.int8), FixedImpl.FP16x16)
y = argmin_use_numpy(x, dtype=np.uint32)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp16x16_2D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = to_fp(np.random.randint(-127, 127, (2, 2)
).astype(np.int8), FixedImpl.FP16x16)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp16x16_2D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = to_fp(np.random.randint(-127, 127, (2, 2)
).astype(np.int8), FixedImpl.FP16x16)
y = argmin_use_numpy_select_last_index(
x, dtype=np.int8)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp16x16_2D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_2D()
def argmin_3D():
def default_params():
x = to_fp(np.random.randint(-127, 127, (2, 2, 2)
).astype(np.int8), FixedImpl.FP16x16)
y = argmin_use_numpy(x, dtype=np.uint32)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp16x16_3D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = to_fp(np.random.randint(-127, 127, (2, 2, 2)
).astype(np.int8), FixedImpl.FP16x16)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp16x16_3D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = to_fp(np.random.randint(-127, 127, (2, 2, 2)
).astype(np.int8), FixedImpl.FP16x16)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp16x16_3D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_3D()
@staticmethod
def argmin_fp8x23():
def argmin_1D():
def default_params():
x = to_fp(np.random.randint(-127, 127, (3)
).astype(np.int8), FixedImpl.FP8x23)
y = argmin_use_numpy(x, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.FP8x23, x.shape,
x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp8x23_1D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = to_fp(np.random.randint(-127, 127, (3)
).astype(np.int8), FixedImpl.FP8x23)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.FP8x23, x.shape,
x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp8x23_1D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = to_fp(np.random.randint(0, 255, (3)).astype(
np.int8), FixedImpl.FP8x23)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32).reshape((1))
x = Tensor(Dtype.FP8x23, x.shape,
x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp8x23_1D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_1D()
def argmin_2D():
def default_params():
x = to_fp(np.random.randint(-127, 127, (2, 2)
).astype(np.int8), FixedImpl.FP8x23)
y = argmin_use_numpy(x, dtype=np.uint32)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp8x23_2D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = to_fp(np.random.randint(-127, 127, (2, 2)
).astype(np.int8), FixedImpl.FP8x23)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32)
x = Tensor(Dtype.FP8x23, x.shape,
x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp8x23_2D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = to_fp(np.random.randint(-127, 127, (2, 2)
).astype(np.int8), FixedImpl.FP8x23)
y = argmin_use_numpy_select_last_index(
x, dtype=np.int8)
x = Tensor(Dtype.FP8x23, x.shape,
x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp8x23_2D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_2D()
def argmin_3D():
def default_params():
x = to_fp(np.random.randint(-127, 127, (2, 2, 2)
).astype(np.int8), FixedImpl.FP8x23)
y = argmin_use_numpy(x, dtype=np.uint32)
x = Tensor(Dtype.FP8x23, x.shape,
x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp8x23_3D_default"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::None(()))", name)
def keepdims_false():
x = to_fp(np.random.randint(-127, 127, (2, 2, 2)
).astype(np.int8), FixedImpl.FP8x23)
y = argmin_use_numpy(
x, keepdims=0, dtype=np.uint32)
x = Tensor(Dtype.FP8x23, x.shape,
x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp8x23_3D_keepdims_false"
make_test(
[x], y, "input_0.argmin(0, Option::Some(false), Option::None(()))", name)
def last_index():
x = to_fp(np.random.randint(-127, 127, (2, 2, 2)
).astype(np.int8), FixedImpl.FP8x23)
y = argmin_use_numpy_select_last_index(
x, dtype=np.uint32)
x = Tensor(Dtype.FP8x23, x.shape,
x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "argmin_fp8x23_3D_last_index"
make_test(
[x], y, "input_0.argmin(0, Option::None(()), Option::Some(true))", name)
default_params()
keepdims_false()
last_index()
argmin_3D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/array_feature_extractor.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Array_feature_extractor(RunAll):
@staticmethod
def array_feature_extractor_3D():
def array_feature_extractor_i32():
x = np.random.randint(-3, 3, (2, 3, 4)).astype(np.int32)
y = np.array([1, 3]).astype(np.uint32)
z = (x[..., y])
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "array_feature_extractor_3D_i32"
make_test([x, y], z, "TensorTrait::array_feature_extractor(@input_0, input_1)", name)
def array_feature_extractor_fp8x23():
x = np.random.randint(-3, 3, (2, 3, 4)).astype(np.float64)
y = np.array([1, 3]).astype(np.uint32)
z = (x[..., y])
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "array_feature_extractor_3D_fp8x23"
make_test([x, y], z, "TensorTrait::array_feature_extractor(@input_0, input_1)", name)
def array_feature_extractor_fp16x16():
x = np.random.randint(-3, 3, (2, 3, 4)).astype(np.float64)
y = np.array([1, 3]).astype(np.uint32)
z = (x[..., y])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "array_feature_extractor_3D_fp16x16"
make_test([x, y], z, "TensorTrait::array_feature_extractor(@input_0, input_1)", name)
array_feature_extractor_i32()
array_feature_extractor_fp8x23()
array_feature_extractor_fp16x16()
@staticmethod
def array_feature_extractor_2D():
def array_feature_extractor_i32():
x = np.random.randint(-3, 3, (3, 4)).astype(np.int32)
y = np.array([1, 3]).astype(np.uint32)
z = (x[..., y])
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "array_feature_extractor_2D_i32"
make_test([x, y], z, "TensorTrait::array_feature_extractor(@input_0, input_1)", name)
def array_feature_extractor_fp8x23():
x = np.random.randint(-3, 3, (3, 4)).astype(np.float64)
y = np.array([1, 3]).astype(np.uint32)
z = (x[..., y])
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "array_feature_extractor_2D_fp8x23"
make_test([x, y], z, "TensorTrait::array_feature_extractor(@input_0, input_1)", name)
def array_feature_extractor_fp16x16():
x = np.random.randint(-3, 3, (3, 4)).astype(np.float64)
y = np.array([1, 3]).astype(np.uint32)
z = (x[..., y])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "array_feature_extractor_2D_fp16x16"
make_test([x, y], z, "TensorTrait::array_feature_extractor(@input_0, input_1)", name)
array_feature_extractor_i32()
array_feature_extractor_fp8x23()
array_feature_extractor_fp16x16()
@staticmethod
def array_feature_extractor_1D():
def array_feature_extractor_i32():
x = np.random.randint(-3, 3, (4)).astype(np.int32)
y = np.array([1, 3]).astype(np.uint32)
z = (x[..., y])
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "array_feature_extractor_1D_i32"
make_test([x, y], z, "TensorTrait::array_feature_extractor(@input_0, input_1)", name)
def array_feature_extractor_fp8x23():
x = np.random.randint(-3, 3, (4)).astype(np.float64)
y = np.array([1, 3]).astype(np.uint32)
z = (x[..., y])
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "array_feature_extractor_1D_fp8x23"
make_test([x, y], z, "TensorTrait::array_feature_extractor(@input_0, input_1)", name)
def array_feature_extractor_fp16x16():
x = np.random.randint(-3, 3, (4)).astype(np.float64)
y = np.array([1, 3]).astype(np.uint32)
z = (x[..., y])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "array_feature_extractor_1D_fp16x16"
make_test([x, y], z, "TensorTrait::array_feature_extractor(@input_0, input_1)", name)
array_feature_extractor_i32()
array_feature_extractor_fp8x23()
array_feature_extractor_fp16x16()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/asin.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Asin(RunAll):
@staticmethod
def asin_fp8x23():
x = np.random.uniform(-1, 1, (2, 2)).astype(np.float64)
y = np.arcsin(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "asin_fp8x23"
make_test([x], y, "input_0.asin()", name)
@staticmethod
def asin_fp16x16():
x = np.random.uniform(-1, 1, (2, 2)).astype(np.float64)
y = np.arcsin(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "asin_fp16x16"
make_test([x], y, "input_0.asin()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/asinh.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Asinh(RunAll):
@staticmethod
def asinh_fp8x23():
x = np.random.uniform(1, 5, (2, 2)).astype(np.float64)
y = np.arcsinh(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "asinh_fp8x23"
make_test([x], y, "input_0.asinh()", name)
@staticmethod
def asinh_fp16x16():
x = np.random.uniform(1, 5, (2, 2)).astype(np.float64)
y = np.arcsinh(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "asinh_fp16x16"
make_test([x], y, "input_0.asinh()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/atan.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Atan(RunAll):
@staticmethod
def atan_fp8x23():
x = np.random.uniform(-10, 127, (2, 2)).astype(np.float64)
y = np.arctan(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "atan_fp8x23"
make_test([x], y, "input_0.atan()", name)
@staticmethod
def atan_fp16x16():
x = np.random.uniform(-10, 127, (2, 2)).astype(np.float64)
y = np.arctan(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "atan_fp16x16"
make_test([x], y, "input_0.atan()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/binarizer.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_node, make_test, to_fp, Tensor, Dtype, FixedImpl
class Binarizer(RunAll):
@staticmethod
def binarizer_fp8x23():
x = np.random.uniform(-3, 3, (3, 3, 3)).astype(np.float64)
threshold = np.float64(1)
y = (x > threshold).astype(np.float64)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "binarizer_fp8x23"
make_node([x], [y], name)
make_test([x], y, "TensorTrait::binarizer(@input_0, Option::Some(FixedTrait::new(8388608, false));", name)
@staticmethod
def binarizer_fp16x16():
x = np.random.uniform(-3, 3, (3, 3, 3)).astype(np.float64)
threshold = np.float64(1)
y = (x > threshold).astype(np.float64)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "binarizer_fp16x16"
make_node([x], [y], name)
make_test([x], y, "TensorTrait::binarizer(@input_0, Option::Some(FixedTrait::new(65536, false));", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/blackman_window.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait, get_data_statement
def blackman_window(size, output_datatype=None, periodic=None) -> np.ndarray: # type: ignore
if periodic == 1:
N_1 = size
else:
N_1 = size - 1
ni = np.arange(size, dtype=output_datatype)
alpha = 0.42
beta = 0.08
y = np.cos((ni * (np.float64(np.pi).astype(output_datatype) * 2)) / N_1).astype(output_datatype) * (-0.5)
y += np.cos((ni * (np.float64(np.pi).astype(output_datatype) * 4)) / N_1) * beta
y += alpha
return y.astype(output_datatype)
class Blackman_window(RunAll):
@staticmethod
# We test here with fp8x23 implementation.
def fp8x23():
args = [3]
# x = np.float64(4)
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP8x23), Dtype.FP8x23)
y = blackman_window(*args, np.float64)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
# Define the name of the generated folder.
name = "blackman_window_fp8x23"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::blackman_window({','.join(args_str)}, Option::Some(0))", # The code signature.
name # The name of the generated folder.
)
@staticmethod
# We test here with fp16x16 implementation.
def fp16x16():
print(get_data_statement(to_fp(np.array([np.pi]).flatten(), FixedImpl.FP16x16), Dtype.FP16x16))
args = [3]
# x = np.float64(4)
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP16x16), Dtype.FP16x16)
y = blackman_window(*args, np.float16, 1)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
# Define the name of the generated folder.
name = "blackman_window_fp16x16"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::blackman_window({','.join(args_str)}, Option::Some(1))", # The code signature.
name # The name of the generated folder.
)
# @staticmethod
# # We test here with i8 implementation.
# def i8():
# print(get_data_statement(np.array([np.pi]).flatten(), Dtype.I8))
# args = [5]
# # x = np.float64(4)
# args_str = get_data_statement(np.array(args).flatten(), Dtype.I8)
# y = blackman_window(*args, np.int8)
# print(y)
# # Convert the floats values in `y` to fixed points with `to_fp` method:
# y = Tensor(Dtype.I8, y.shape, y.flatten())
# # Define the name of the generated folder.
# name = "blackman_window_i8"
# # Invoke `make_test` method to generate corresponding Cairo tests:
# make_test(
# [], # List of input tensors.
# y, # The expected output result.
# f"TensorTrait::blackman_window({','.join(args_str)}, Option::Some(1))", # The code signature.
# name # The name of the generated folder.
# )
# @staticmethod
# # We test here with i32 implementation.
# def i32():
# print(get_data_statement(np.array([np.pi]).flatten(), Dtype.I32))
# args = [4]
# # x = np.float64(4)
# args_str = get_data_statement(np.array(args).flatten(), Dtype.I32)
# y = blackman_window(*args, np.int32)
# print(y)
# # Convert the floats values in `y` to fixed points with `to_fp` method:
# y = Tensor(Dtype.I32, y.shape, y.flatten())
# # Define the name of the generated folder.
# name = "blackman_window_i32"
# # Invoke `make_test` method to generate corresponding Cairo tests:
# make_test(
# [], # List of input tensors.
# y, # The expected output result.
# f"TensorTrait::blackman_window({','.join(args_str)}, Option::Some(0))", # The code signature.
# name # The name of the generated folder.
# )
# @staticmethod
# # We test here with u32 implementation.
# def u32():
# print(get_data_statement(np.array([np.pi]).flatten(), Dtype.U32))
# args = [4]
# # x = np.float64(4)
# args_str = get_data_statement(np.array(args).flatten(), Dtype.U32)
# y = blackman_window(*args, np.uint32)
# print(y)
# # Convert the floats values in `y` to fixed points with `to_fp` method:
# y = Tensor(Dtype.U32, y.shape, y.flatten())
# # Define the name of the generated folder.
# name = "blackman_window_u32"
# # Invoke `make_test` method to generate corresponding Cairo tests:
# make_test(
# [], # List of input tensors.
# y, # The expected output result.
# f"TensorTrait::blackman_window({','.join(args_str)}, Option::Some(0))", # The code signature.
# name # The name of the generated folder.
# )
|
https://github.com/gizatechxyz/orion
|
nodegen/node/ceil.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Ceil(RunAll):
@staticmethod
def ceil_fp8x23():
x = np.random.uniform(-1, 1, (2, 2)).astype(np.float64)
y = np.ceil(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "ceil_fp8x23"
make_test([x], y, "input_0.ceil()", name)
@staticmethod
def ceil_fp16x16():
x = np.random.uniform(-1, 1, (2, 2)).astype(np.float64)
y = np.ceil(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "ceil_fp16x16"
make_test([x], y, "input_0.ceil()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/clip.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Clip(RunAll):
@staticmethod
def clip_u32():
def clip_2D():
x = np.random.randint(0, 255, (2, 4)).astype(np.uint32)
y = np.clip(x, np.uint32(10), np.uint32(20))
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "clip_u32_2d"
make_test(
[x], y, "input_0.clip(Option::Some(10_u32), Option::Some(20_u32))", name)
def clip_3D():
x = np.random.randint(0, 255, (20, 10, 5)).astype(np.uint32)
y = np.clip(x, np.uint32(10), np.uint32(20))
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "clip_u32_3d"
make_test(
[x], y, "input_0.clip(Option::Some(10_u32), Option::Some(20_u32))", name)
clip_2D()
clip_3D()
@staticmethod
def clip_i32():
def clip_2D():
x = np.random.randint(-127, 127, (2, 4)).astype(np.int32)
y = np.clip(x, np.int32(-10), np.int32(20))
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "clip_i32_2d"
make_test(
[x], y, "input_0.clip(Option::Some(-10_i32), Option::Some(20_i32))", name)
def clip_3D():
x = np.random.randint(-127, 127, (20, 10, 5)).astype(np.int32)
y = np.clip(x, np.int32(-10), np.int32(20))
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "clip_i32_3d"
make_test(
[x], y, "input_0.clip(Option::Some(-10_i32), Option::Some(20_i32))", name)
clip_2D()
clip_3D()
@staticmethod
def clip_i8():
def clip_2D():
x = np.random.randint(-127, 127, (2, 4)).astype(np.int8)
y = np.clip(x, np.int8(-10), np.int8(20))
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "clip_i8_2d"
make_test(
[x], y, "input_0.clip(Option::Some(-10_i8), Option::Some(20_i8))", name)
def clip_3D():
x = np.random.randint(-127, 127, (20, 10, 5)).astype(np.int8)
y = np.clip(x, np.int8(-10), np.int8(20))
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "clip_i8_3d"
make_test(
[x], y, "input_0.clip(Option::Some(-10_i8), Option::Some(20_i8))", name)
clip_2D()
clip_3D()
@staticmethod
def clip_fp8x23():
def clip_2D():
x = to_fp(np.random.randint(-127, 127, (2, 4)
).astype(np.int64), FixedImpl.FP8x23)
y = np.clip(x, to_fp(np.int64(-10), FixedImpl.FP8x23), to_fp(np.int64(20), FixedImpl.FP8x23))
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "clip_fp8x23_2d"
make_test(
[x], y, "input_0.clip(Option::Some(FP8x23 { mag: 83886080, sign: true }), Option::Some(FP8x23 { mag: 167772160, sign: false }))", name)
def clip_3D():
x = to_fp(np.random.randint(-127, 127, (20, 10, 5)
).astype(np.int64), FixedImpl.FP8x23)
y = np.clip(x, to_fp(np.int64(-10), FixedImpl.FP8x23), to_fp(np.int64(20), FixedImpl.FP8x23))
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "clip_fp8x23_3d"
make_test(
[x], y, "input_0.clip(Option::Some(FP8x23 { mag: 83886080, sign: true }), Option::Some(FP8x23 { mag: 167772160, sign: false }))", name)
clip_2D()
clip_3D()
@staticmethod
def clip_fp16x16():
def clip_2D():
x = to_fp(np.random.randint(-127, 127, (2, 4)
).astype(np.int64), FixedImpl.FP16x16)
y = np.clip(x, to_fp(np.int64(-10), FixedImpl.FP16x16), to_fp(np.int64(20), FixedImpl.FP16x16))
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "clip_fp16x16_2d"
make_test(
[x], y, "input_0.clip(Option::Some(FP16x16 { mag: 655360, sign: true }), Option::Some(FP16x16 { mag: 1310720, sign: false }))", name)
def clip_3D():
x = to_fp(np.random.randint(-127, 127, (20, 10, 5)
).astype(np.int64), FixedImpl.FP16x16)
y = np.clip(x, to_fp(np.int64(-10), FixedImpl.FP16x16), to_fp(np.int64(20), FixedImpl.FP16x16))
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "clip_fp16x16_3d"
make_test(
[x], y, "input_0.clip(Option::Some(FP16x16 { mag: 655360, sign: true }), Option::Some(FP16x16 { mag: 1310720, sign: false }))", name)
clip_2D()
clip_3D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/col2im.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def col2im(data, image_shape, block_shape, dilations=None, pads=None, strides=None): # type: ignore
if dilations is None:
dilations = [1 for s in image_shape]
if pads is None:
pads = [0 for s in image_shape] * 2
if strides is None:
strides = [1 for s in image_shape]
bl = np.prod(block_shape)
C = data.shape[1] // bl
data = data.reshape(data.shape[:1] + (C,) + (bl,) + data.shape[2:])
ks = tuple(block_shape)
res = None
for n in range(data.shape[0]):
for c in range(data.shape[1]):
out = col2im_naive_implementation(
data[n, c, ...], image_shape, ks, dilations, pads, strides
)
if res is None:
new_shape = data.shape[:2] + out.shape
res = np.empty(new_shape, dtype=data.dtype)
res[n, c, ...] = out
return (res,) # type: ignore
def _get_indices(i, shape):
res = np.empty((len(shape),), dtype=np.int64)
k = len(shape) - 1
while k > 0:
m = i % shape[k]
res[k] = m
i -= m
i /= shape[k]
k -= 1
res[0] = i
return res
def _col2im_shape_check(X, output_shape, kernel_shape, dilations, pads, strides): # type: ignore
n_input_plane = X.shape[0]
kernel_size = np.prod(kernel_shape)
if n_input_plane % kernel_size != 0:
raise ValueError(
f"Expected size of input's dimension 1 to be divisible by the "
f"product of kernel_size={kernel_size}, "
f"but got input.size(1)={n_input_plane} "
f"and kernel_shape={kernel_shape}, X.shape={X.shape}, output_shape={output_shape}."
)
input_length = X.shape[1]
n_dims = len(output_shape)
n_blocks = []
for i in range(n_dims):
n_block = (
output_shape[i]
+ pads[i, :].sum()
- dilations[i] * (kernel_shape[i] - 1)
- 1
) // strides[i] + 1
n_blocks.append(n_block)
block_size = np.prod(n_blocks)
if input_length != block_size:
raise ValueError(
f"Given n_input_plane={n_input_plane}, X.shape={X.shape}, "
f"output_shape={output_shape}, kernel_shape={kernel_shape}, "
f"dilations={dilations}, pads={pads}, strides={strides}, "
f"expected size of input's dimension 2 to match the calculated number of "
f"sliding blocks {n_blocks} = {block_size}, "
f"but got input.size(2)={input_length}.",
)
def col2im_naive_implementation(data, image_shape, kernel_shape, dilations, pads, strides): # type: ignore
n_dims = len(pads) // 2
new_pads = np.array([(pads[i], pads[i + n_dims]) for i in range(n_dims)])
_col2im_shape_check(data, image_shape, kernel_shape, dilations, new_pads, strides)
data_col = data
data_im = np.zeros(image_shape, dtype=data.dtype)
dim_col = []
for i in range(n_dims):
col = (
image_shape[i]
+ new_pads[i, :].sum()
- (dilations[i] * (kernel_shape[i] - 1) + 1)
) // strides[i] + 1
dim_col.append(col)
kernel_size = np.prod(kernel_shape)
col_size = np.prod(dim_col)
for c_col in range(kernel_size):
offset = _get_indices(c_col, kernel_shape)
for col in range(col_size):
ind_col = _get_indices(col, dim_col)
ind_im = []
for i in range(n_dims):
ind = (
ind_col[i] * strides[i] - new_pads[i, 0] + offset[i] * dilations[i]
)
ind_im.append(ind)
if not _is_out(ind_im, data_im.shape):
data_im[tuple(ind_im)] += data_col[c_col, col]
return data_im
def _is_out(ind, shape):
for i, s in zip(ind, shape):
if i < 0:
return True
if i >= s:
return True
return False
class Col2im(RunAll):
@staticmethod
def export_col2im() -> None:
x = np.array(
[
[
[1.0, 6.0, 11.0, 16.0, 21.0], # (1, 5, 5)
[2.0, 7.0, 12.0, 17.0, 22.0],
[3.0, 8.0, 13.0, 18.0, 23.0],
[4.0, 9.0, 14.0, 19.0, 24.0],
[5.0, 0.0, 15.0, 20.0, 25.0],
]
]
).astype(np.float32)
image_shape = np.array([5, 5]).astype(np.int64)
block_shape = np.array([1, 5]).astype(np.int64)
y = col2im(x,image_shape,block_shape)
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "col2im"
func_sig = "NNTrait::col2im("
func_sig += "@input_0,"
func_sig += "array![5, 5].span(),"
func_sig += "array![1, 5].span(),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x], y, func_sig, name, Trait.NN)
@staticmethod
def export_col2im_strides() -> None:
x = np.array(
[
[
[0.0, 0.0, 0.0, 0.0], # (1, 9, 4)
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 1.0, 1.0],
[0.0, 0.0, 0.0, 0.0],
]
]
).astype(np.float32)
image_shape = np.array([5, 5]).astype(np.int64)
block_shape = np.array([3, 3]).astype(np.int64)
y = col2im(x,image_shape,block_shape,strides=[2, 2])
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "col2im_strides"
func_sig = "NNTrait::col2im("
func_sig += "@input_0,"
func_sig += "array![5, 5].span(),"
func_sig += "array![3, 3].span(),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![2, 2].span()))"
make_test(
[x], y, func_sig, name, Trait.NN)
@staticmethod
def export_col2im_pads() -> None:
x = np.array(
[
[
[
1.0, 6.0, 11.0, 16.0, 21.0, 26, 31, 36, 41, 46, 51, 56, 61, 66, 71,
], # (1, 5, 15)
[
2.0, 7.0, 12.0, 17.0, 22.0, 27, 32, 37, 42, 47, 52, 57, 62, 67, 72,
],
[
3.0, 8.0, 13.0, 18.0, 23.0, 28, 33, 38, 43, 48, 53, 58, 63, 68, 73,
],
[
4.0, 9.0, 14.0, 19.0, 24.0, 29, 34, 39, 44, 49, 54, 59, 64, 69, 74,
],
[
5.0, 10.0, 15.0, 20.0, 25.0, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75,
],
]
]
).astype(np.float32)
image_shape = np.array([5, 5]).astype(np.int64)
block_shape = np.array([1, 5]).astype(np.int64)
y = col2im(x,image_shape,block_shape,pads=[0, 1, 0, 1])
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "col2im_pads"
func_sig = "NNTrait::col2im("
func_sig += "@input_0,"
func_sig += "array![5, 5].span(),"
func_sig += "array![1, 5].span(),"
func_sig += "Option::None,"
func_sig += "Option::Some(array![0, 1, 0, 1].span()),"
func_sig += "Option::None)"
make_test(
[x], y, func_sig, name, Trait.NN)
@staticmethod
def export_col2im_dilations() -> None:
x = np.array(
[
[
[1.0, 5.0, 9.0, 13.0, 17], # (1, 4, 5)
[2.0, 6.0, 10.0, 14.0, 18],
[3.0, 7.0, 11.0, 15.0, 19],
[4.0, 8.0, 12.0, 16.0, 20],
]
]
).astype(np.float32)
image_shape = np.array([6, 6]).astype(np.int64)
block_shape = np.array([2, 2]).astype(np.int64)
y = col2im(x,image_shape,block_shape, dilations=[1, 5])
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "col2im_dilations"
func_sig = "NNTrait::col2im("
func_sig += "@input_0,"
func_sig += "array![6, 6].span(),"
func_sig += "array![2, 2].span(),"
func_sig += "Option::Some(array![1, 5].span()),"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x], y, func_sig, name, Trait.NN)
@staticmethod
def export_col2im_5D() -> None:
x = np.array(
[
[
[1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56], # (1, 10, 12)
[2, 7, 12, 17, 22, 27, 32, 37, 42, 47, 52, 57],
[3, 8, 13, 18, 23, 28, 33, 38, 43, 48, 53, 58],
[4, 9, 14, 19, 24, 29, 34, 39, 44, 49, 54, 59],
[5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60],
[61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116],
[62, 67, 72, 77, 82, 87, 92, 97, 102, 107, 112, 117],
[63, 68, 73, 78, 83, 88, 93, 98, 103, 108, 113, 118],
[64, 69, 74, 79, 84, 89, 94, 99, 104, 109, 114, 119],
[65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120],
]
]
).astype(np.float32)
image_shape = np.array([3, 4, 5]).astype(np.int64)
block_shape = np.array([1, 1, 5]).astype(np.int64)
y = col2im(x,image_shape,block_shape)
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "col2im_5D"
func_sig = "NNTrait::col2im("
func_sig += "@input_0,"
func_sig += "array![3, 4, 5].span(),"
func_sig += "array![1, 1, 5].span(),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x], y, func_sig, name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/compress.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
class Compress(RunAll):
@staticmethod
def compress_fp16x16():
def compress_3D():
def default():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.array([0, 1, 1]).astype(np.uint32)
y = x1.compress(x2, axis=0)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "compress_fp16x16_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(0))",
name= name)
def axis1():
x1 = np.arange(0,180).reshape(3,4,3,5).astype(np.int64)
x2 = np.array([1, 1, 1, 0]).astype(np.int64)
y = x1.compress(x2, axis=1)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "compress_fp16x16_3d_axis1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(1))",
name= name)
def axis2():
x1 = np.arange(0,48).reshape(4,3,4).astype(np.int64)
x2 = np.array([1, 0, 1, 1]).astype(np.int64)
y = x1.compress(x2, axis=2)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "compress_fp16x16_3d_axis2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))",
name= name)
def axis3():
x1 = np.arange(0,96).reshape(4,3,4, 2).astype(np.int64)
x2 = np.array([1, 0]).astype(np.int64)
y = x1.compress(x2, axis=3)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "compress_fp16x16_3d_axis3"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(3))",
name= name)
def noaxis():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.array([1, 0, 1, 0, 1, 1, 1, 1, 1]).astype(np.int64)
y = x1.compress(x2)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "compress_fp16x16_3d_noaxis"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::None(()))",
name= name)
default()
axis1()
axis2()
axis3()
noaxis()
compress_3D()
@staticmethod
def compress_fp8x23():
def compress_3D():
def default():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.array([0, 1, 1]).astype(np.uint32)
y = x1.compress(x2, axis=0)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "compress_fp8x23_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(0))",
name= name)
def axis1():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.array([0, 1, 1]).astype(np.uint32)
y = x1.compress(x2, axis=1)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "compress_fp8x23_3d_axis1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(1))",
name= name)
def axis2():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.array([0, 1, 1]).astype(np.uint32)
y = x1.compress(x2, axis=2)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "compress_fp8x23_3d_axis2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))",
name= name)
default()
axis1()
axis2()
compress_3D()
@staticmethod
def compress_i8():
def compress_3D():
def default():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int8)
x2 = np.array([0, 1, 1]).astype(np.uint8)
y = x1.compress(x2, axis=0)
x1 = Tensor(Dtype.I8, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "compress_i8_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(0))",
name= name)
def axis1():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int8)
x2 = np.array([0, 1, 1]).astype(np.uint8)
y = x1.compress(x2, axis=1)
x1 = Tensor(Dtype.I8, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "compress_i8_3d_axis1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(1))",
name= name)
def axis2():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int8)
x2 = np.array([0, 1, 1]).astype(np.uint8)
y = x1.compress(x2, axis=2)
x1 = Tensor(Dtype.I8, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "compress_i8_3d_axis2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))",
name= name)
default()
axis1()
axis2()
compress_3D()
@staticmethod
def compress_i32():
def compress_3D():
def default():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int32)
x2 = np.array([0, 1, 1]).astype(np.int32)
y = x1.compress(x2, axis=0)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "compress_i32_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(0))",
name= name)
def axis1():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int32)
x2 = np.array([0, 1, 1]).astype(np.int32)
y = x1.compress(x2, axis=1)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "compress_i32_3d_axis1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(1))",
name= name)
def axis2():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int32)
x2 = np.array([0, 1, 1]).astype(np.int32)
y = x1.compress(x2, axis=2)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "compress_i32_3d_axis2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))",
name= name)
default()
axis1()
axis2()
compress_3D()
@staticmethod
def compress_u32():
def compress_3D():
def default():
x1 = np.arange(0,48).reshape(4,4,3).astype(np.uint32)
x2 = np.array([1, 1]).astype(np.uint32)
y = x1.compress(x2, axis=0)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "compress_u32_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(0))",
name= name)
def axis1():
x1 = np.arange(0,36).reshape(3,4,3).astype(np.uint32)
x2 = np.array([0, 1, 1]).astype(np.uint32)
y = x1.compress(x2, axis=1)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "compress_u32_3d_axis1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(1))",
name= name)
def axis2():
x1 = np.arange(0,48).reshape(3,4,4).astype(np.uint32)
x2 = np.array([0, 1, 1]).astype(np.uint32)
y = x1.compress(x2, axis=2)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "compress_u32_3d_axis2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))",
name= name)
def axis2_2():
x1 = np.arange(0,60).reshape(3,4,5).astype(np.uint32)
x2 = np.array([0, 1, 1]).astype(np.uint32)
y = x1.compress(x2, axis=2)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "compress_u32_3d_axis2_2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(2))",
name= name)
def axis3():
x1 = np.arange(0,270).reshape(3,3,5,6).astype(np.uint32)
x2 = np.array([0, 1, 1,1,0,1]).astype(np.uint32)
y = x1.compress(x2, axis=3)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "compress_u32_3d_axis3"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.compress(condition:input_1, axis:Option::Some(3))",
name= name)
default()
axis1()
axis2()
axis2_2()
axis3()
compress_3D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/concat.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
# 687
class Concat(RunAll):
@staticmethod
def concat_u32():
def concat_1D():
x1 = np.arange(0,3).astype(np.uint32)
x2 = np.arange(3,6).astype(np.uint32)
y = np.concatenate((x1, x2))
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "concat_u32_1d"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def concat_2D():
x1 = np.arange(0,4).astype(np.uint32).reshape(2,2)
x2 = np.arange(4,8).astype(np.uint32).reshape(2,2)
y = np.concatenate((x1, x2), axis=0)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "concat_u32_2d"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def concat_3D():
def default():
x1 = np.arange(0,27).astype(np.uint32).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.uint32).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=0)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "concat_u32_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def axis_1():
x1 = np.arange(0,27).astype(np.uint32).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.uint32).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=1)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "concat_u32_3d_axis_1"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 1)",
name= name, trait= Trait.TENSOR)
def axis_2():
x1 = np.arange(0,27).astype(np.uint32).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.uint32).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=2)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "concat_u32_3d_axis_2"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 2)",
name= name, trait= Trait.TENSOR)
def three_tensors_axis_1():
x1 = np.arange(0,27).astype(np.uint32).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.uint32).reshape(3,3,3)
x3 = np.arange(54,81).astype(np.uint32).reshape(3,3,3)
y = np.concatenate((x1, x2, x3), axis=1)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "concat_u32_3d_three_tensors_axis_1"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1, input_2].span(), 1)",
name= name, trait= Trait.TENSOR)
def three_tensors_axis_2():
x1 = np.arange(0,27).astype(np.uint32).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.uint32).reshape(3,3,3)
x3 = np.arange(54,81).astype(np.uint32).reshape(3,3,3)
y = np.concatenate((x1, x2, x3), axis=2)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "concat_u32_3d_three_tensors_axis_2"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1, input_2].span(), 2)",
name= name, trait= Trait.TENSOR)
default()
axis_1()
axis_2()
three_tensors_axis_1()
three_tensors_axis_2()
concat_1D()
concat_2D()
concat_3D()
@staticmethod
def concat_i32():
def concat_1D():
x1 = np.arange(0,3).astype(np.int32)
x2 = np.arange(3,6).astype(np.int32)
y = np.concatenate((x1, x2))
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "concat_i32_1d"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR.TENSOR)
def concat_2D():
x1 = np.arange(0,4).astype(np.int32).reshape(2,2)
x2 = np.arange(4,8).astype(np.int32).reshape(2,2)
y = np.concatenate((x1, x2), axis=0)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "concat_i32_2d"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def concat_3D():
def default():
x1 = np.arange(0,27).astype(np.int32).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int32).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=0)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "concat_i32_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def axis_1():
x1 = np.arange(0,27).astype(np.int32).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int32).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=1)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "concat_i32_3d_axis_1"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 1)",
name= name, trait= Trait.TENSOR)
def axis_2():
x1 = np.arange(0,27).astype(np.int32).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int32).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=2)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "concat_i32_3d_axis_2"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 2)",
name= name, trait= Trait.TENSOR)
def three_tensors_axis_1():
x1 = np.arange(0,27).astype(np.int32).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int32).reshape(3,3,3)
x3 = np.arange(54,81).astype(np.int32).reshape(3,3,3)
y = np.concatenate((x1, x2, x3), axis=1)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.I32, x3.shape, x3.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "concat_i32_3d_three_tensors_axis_1"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1, input_2].span(), 1)",
name= name, trait= Trait.TENSOR)
def three_tensors_axis_2():
x1 = np.arange(0,27).astype(np.int32).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int32).reshape(3,3,3)
x3 = np.arange(54,81).astype(np.int32).reshape(3,3,3)
y = np.concatenate((x1, x2, x3), axis=2)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.I32, x3.shape, x3.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "concat_i32_3d_three_tensors_axis_2"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1, input_2].span(), 2)",
name= name, trait= Trait.TENSOR)
default()
axis_1()
axis_2()
three_tensors_axis_1()
three_tensors_axis_2()
concat_1D()
concat_2D()
concat_3D()
@staticmethod
def concat_i8():
def concat_1D():
x1 = np.arange(0,3).astype(np.int8)
x2 = np.arange(3,6).astype(np.int8)
y = np.concatenate((x1, x2))
x1 = Tensor(Dtype.FP8x23, x1.shape, x1.flatten())
x2 = Tensor(Dtype.FP8x23, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "concat_i8_1d"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR.TENSOR)
def concat_2D():
x1 = np.arange(0,4).astype(np.int8).reshape(2,2)
x2 = np.arange(4,8).astype(np.int8).reshape(2,2)
y = np.concatenate((x1, x2), axis=0)
x1 = Tensor(Dtype.FP8x23, x1.shape, x1.flatten())
x2 = Tensor(Dtype.FP8x23, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "concat_i8_2d"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def concat_3D():
def default():
x1 = np.arange(0,27).astype(np.int8).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int8).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=0)
x1 = Tensor(Dtype.FP8x23, x1.shape, x1.flatten())
x2 = Tensor(Dtype.FP8x23, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "concat_i8_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def axis_1():
x1 = np.arange(0,27).astype(np.int8).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int8).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=1)
x1 = Tensor(Dtype.FP8x23, x1.shape, x1.flatten())
x2 = Tensor(Dtype.FP8x23, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "concat_i8_3d_axis_1"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 1)",
name= name, trait= Trait.TENSOR)
def axis_2():
x1 = np.arange(0,27).astype(np.int8).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int8).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=2)
x1 = Tensor(Dtype.FP8x23, x1.shape, x1.flatten())
x2 = Tensor(Dtype.FP8x23, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "concat_i8_3d_axis_2"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 2)",
name= name, trait= Trait.TENSOR)
def three_tensors_axis_1():
x1 = np.arange(0,27).astype(np.int8).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int8).reshape(3,3,3)
x3 = np.arange(54,81).astype(np.int8).reshape(3,3,3)
y = np.concatenate((x1, x2, x3), axis=1)
x1 = Tensor(Dtype.FP8x23, x1.shape, x1.flatten())
x2 = Tensor(Dtype.FP8x23, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP8x23, x3.shape, x3.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "concat_i8_3d_three_tensors_axis_1"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1, input_2].span(), 1)",
name= name, trait= Trait.TENSOR)
def three_tensors_axis_2():
x1 = np.arange(0,27).astype(np.int8).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int8).reshape(3,3,3)
x3 = np.arange(54,81).astype(np.int8).reshape(3,3,3)
y = np.concatenate((x1, x2, x3), axis=2)
x1 = Tensor(Dtype.FP8x23, x1.shape, x1.flatten())
x2 = Tensor(Dtype.FP8x23, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP8x23, x3.shape, x3.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "concat_i8_3d_three_tensors_axis_2"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1, input_2].span(), 2)",
name= name, trait= Trait.TENSOR)
default()
axis_1()
axis_2()
three_tensors_axis_1()
three_tensors_axis_2()
concat_1D()
concat_2D()
concat_3D()
@staticmethod
def concat_fp8x23():
def concat_1D():
x1 = np.arange(0,3).astype(np.int64)
x2 = np.arange(3,6).astype(np.int64)
y = np.concatenate((x1, x2))
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.FP8x23, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "concat_fp8x23_1d"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR.TENSOR)
def concat_2D():
x1 = np.arange(0,4).astype(np.int64).reshape(2,2)
x2 = np.arange(4,8).astype(np.int64).reshape(2,2)
y = np.concatenate((x1, x2), axis=0)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.FP8x23, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "concat_fp8x23_2d"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def concat_3D():
def default():
x1 = np.arange(0,27).astype(np.int64).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int64).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=0)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.FP8x23, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape,to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "concat_fp8x23_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def axis_1():
x1 = np.arange(0,27).astype(np.int64).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int64).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=1)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.FP8x23, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "concat_fp8x23_3d_axis_1"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 1)",
name= name, trait= Trait.TENSOR)
def axis_2():
x1 = np.arange(0,27).astype(np.int64).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int64).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=2)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.FP8x23, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "concat_fp8x23_3d_axis_2"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 2)",
name= name, trait= Trait.TENSOR)
def three_tensors_axis_1():
x1 = np.arange(0,27).astype(np.int64).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int64).reshape(3,3,3)
x3 = np.arange(54,81).astype(np.int64).reshape(3,3,3)
y = np.concatenate((x1, x2, x3), axis=1)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.FP8x23, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP8x23))
x3 = Tensor(Dtype.FP8x23, x3.shape,to_fp(
x3.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "concat_fp8x23_3d_three_tensors_axis_1"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1, input_2].span(), 1)",
name= name, trait= Trait.TENSOR)
def three_tensors_axis_2():
x1 = np.arange(0,27).astype(np.int64).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int64).reshape(3,3,3)
x3 = np.arange(54,81).astype(np.int64).reshape(3,3,3)
y = np.concatenate((x1, x2, x3), axis=2)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.FP8x23, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP8x23))
x3 = Tensor(Dtype.FP8x23, x3.shape, to_fp(
x3.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "concat_fp8x23_3d_three_tensors_axis_2"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1, input_2].span(), 2)",
name= name, trait= Trait.TENSOR)
default()
axis_1()
axis_2()
three_tensors_axis_1()
three_tensors_axis_2()
concat_1D()
concat_2D()
concat_3D()
staticmethod
def concat_fp16x16():
def concat_1D():
x1 = np.arange(0,3).astype(np.int64)
x2 = np.arange(3,6).astype(np.int64)
y = np.concatenate((x1, x2))
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.FP16x16, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "concat_fp16x16_1d"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR.TENSOR)
def concat_2D():
x1 = np.arange(0,4).astype(np.int64).reshape(2,2)
x2 = np.arange(4,8).astype(np.int64).reshape(2,2)
y = np.concatenate((x1, x2), axis=0)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.FP16x16, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "concat_fp16x16_2d"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def concat_3D():
def default():
x1 = np.arange(0,27).astype(np.int64).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int64).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=0)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.FP16x16, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "concat_fp16x16_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 0)",
name= name, trait= Trait.TENSOR)
def axis_1():
x1 = np.arange(0,27).astype(np.int64).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int64).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=1)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.FP16x16, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape ,to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "concat_fp16x16_3d_axis_1"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 1)",
name= name, trait= Trait.TENSOR)
def axis_2():
x1 = np.arange(0,27).astype(np.int64).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int64).reshape(3,3,3)
y = np.concatenate((x1, x2), axis=2)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.FP16x16, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "concat_fp16x16_3d_axis_2"
make_test(
inputs = [x1, x2], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1].span(), 2)",
name= name, trait= Trait.TENSOR)
def three_tensors_axis_1():
x1 = np.arange(0,27).astype(np.int64).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int64).reshape(3,3,3)
x3 = np.arange(54,81).astype(np.int64).reshape(3,3,3)
y = np.concatenate((x1, x2, x3), axis=1)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.FP16x16, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP16x16))
x3 = Tensor(Dtype.FP16x16, x3.shape, to_fp(
x3.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "concat_fp16x16_3d_three_tensors_axis_1"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1, input_2].span(), 1)",
name= name, trait= Trait.TENSOR)
def three_tensors_axis_2():
x1 = np.arange(0,27).astype(np.int64).reshape(3,3,3)
x2 = np.arange(27,54).astype(np.int64).reshape(3,3,3)
x3 = np.arange(54,81).astype(np.int64).reshape(3,3,3)
y = np.concatenate((x1, x2, x3), axis=2)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(
x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.FP16x16, x2.shape, to_fp(
x2.flatten(), FixedImpl.FP16x16))
x3 = Tensor(Dtype.FP16x16, x3.shape, to_fp(
x3.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape,to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "concat_fp16x16_3d_three_tensors_axis_2"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "TensorTrait::concat(array![input_0, input_1, input_2].span(), 2)",
name= name, trait= Trait.TENSOR)
default()
axis_1()
axis_2()
three_tensors_axis_1()
three_tensors_axis_2()
concat_1D()
concat_2D()
concat_3D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/concat_from_sequence.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
class Concat_from_sequence(RunAll):
@staticmethod
def concat_from_sequence_u32():
def new_axis_zero():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(0)
concatenated_tensor = np.concatenate(values_array, axis)
concatenated_tensor = Tensor(Dtype.U32, concatenated_tensor.shape, concatenated_tensor.flatten())
name = "concat_from_sequence_u32_new_axis_zero"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::Some(0))", name, Trait.SEQUENCE)
def new_axis_one():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(1)
concatenated_tensor = np.stack(values_array, axis)
concatenated_tensor = Tensor(Dtype.U32, concatenated_tensor.shape, concatenated_tensor.flatten())
name = "concat_from_sequence_u32_new_axis_one"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::Some(1))", name, Trait.SEQUENCE)
def new_axis_default():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(0)
concatenated_tensor = np.concatenate(values_array, axis)
concatenated_tensor = Tensor(Dtype.U32, concatenated_tensor.shape, concatenated_tensor.flatten())
name = "concat_from_sequence_u32_new_axis_default"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::None(()))", name, Trait.SEQUENCE)
new_axis_zero()
new_axis_one()
new_axis_default()
@staticmethod
def concat_from_sequence_i32():
def new_axis_zero():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(0)
concatenated_tensor = np.concatenate(values_array, axis)
concatenated_tensor = Tensor(Dtype.I32, concatenated_tensor.shape, concatenated_tensor.flatten())
name = "concat_from_sequence_i32_new_axis_zero"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::Some(0))", name, Trait.SEQUENCE)
def new_axis_one():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(1)
concatenated_tensor = np.stack(values_array, axis)
concatenated_tensor = Tensor(Dtype.I32, concatenated_tensor.shape, concatenated_tensor.flatten())
name = "concat_from_sequence_i32_new_axis_one"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::Some(1))", name, Trait.SEQUENCE)
def new_axis_default():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(0)
concatenated_tensor = np.concatenate(values_array, axis)
concatenated_tensor = Tensor(Dtype.I32, concatenated_tensor.shape, concatenated_tensor.flatten())
name = "concat_from_sequence_i32_new_axis_default"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::None(()))", name, Trait.SEQUENCE)
new_axis_zero()
new_axis_one()
new_axis_default()
@staticmethod
def concat_from_sequence_i8():
def new_axis_zero():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(0)
concatenated_tensor = np.concatenate(values_array, axis)
concatenated_tensor = Tensor(Dtype.I8, concatenated_tensor.shape, concatenated_tensor.flatten())
name = "concat_from_sequence_i8_new_axis_zero"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::Some(0))", name, Trait.SEQUENCE)
def new_axis_one():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(1)
concatenated_tensor = np.stack(values_array, axis)
concatenated_tensor = Tensor(Dtype.I8, concatenated_tensor.shape, concatenated_tensor.flatten())
name = "concat_from_sequence_i8_new_axis_one"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::Some(1))", name, Trait.SEQUENCE)
def new_axis_default():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(0)
concatenated_tensor = np.concatenate(values_array, axis)
concatenated_tensor = Tensor(Dtype.I8, concatenated_tensor.shape, concatenated_tensor.flatten())
name = "concat_from_sequence_i8_new_axis_default"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::None(()))", name, Trait.SEQUENCE)
new_axis_zero()
new_axis_one()
new_axis_default()
@staticmethod
def concat_from_sequence_fp8x23():
def new_axis_zero():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(0)
concatenated_tensor = np.concatenate(values_array, axis)
concatenated_tensor = Tensor(Dtype.FP8x23, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP8x23))
name = "concat_from_sequence_fp8x23_new_axis_zero"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::Some(0))", name, Trait.SEQUENCE)
def new_axis_one():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(1)
concatenated_tensor = np.stack(values_array, axis)
concatenated_tensor = Tensor(Dtype.FP8x23, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP8x23))
name = "concat_from_sequence_fp8x23_new_axis_one"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::Some(1))", name, Trait.SEQUENCE)
def new_axis_default():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(0)
concatenated_tensor = np.concatenate(values_array, axis)
concatenated_tensor = Tensor(Dtype.FP8x23, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP8x23))
name = "concat_from_sequence_fp8x23_new_axis_default"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::None(()))", name, Trait.SEQUENCE)
new_axis_zero()
new_axis_one()
new_axis_default()
@staticmethod
def concat_from_sequence_fp16x16():
def new_axis_zero():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(0)
concatenated_tensor = np.concatenate(values_array, axis)
concatenated_tensor = Tensor(Dtype.FP16x16, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP16x16))
name = "concat_from_sequence_fp16x16_new_axis_zero"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::Some(0))", name, Trait.SEQUENCE)
def new_axis_one():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(1)
concatenated_tensor = np.stack(values_array, axis)
concatenated_tensor = Tensor(Dtype.FP16x16, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP16x16))
name = "concat_from_sequence_fp16x16_new_axis_one"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::Some(1))", name, Trait.SEQUENCE)
def new_axis_default():
sequence = []
values_array = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
values_array.append(values)
axis = np.int32(1)
new_axis = np.uint32(0)
concatenated_tensor = np.concatenate(values_array, axis)
concatenated_tensor = Tensor(Dtype.FP16x16, concatenated_tensor.shape, to_fp(concatenated_tensor.flatten(), FixedImpl.FP16x16))
name = "concat_from_sequence_fp16x16_new_axis_default"
make_test([sequence], concatenated_tensor, "SequenceTrait::concat_from_sequence(input_0, 1_i32, Option::None(()))", name, Trait.SEQUENCE)
new_axis_zero()
new_axis_one()
new_axis_default()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/conv.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
import numpy as np
def r_index_check(r_index, shape_out):
for i in range(len(r_index)):
if r_index[i] >= shape_out[i]:
return False
return True
def stride(arr):
stride = np.zeros(len(arr))
acc = 1
for i in range(len(arr)):
stride[i] = acc
acc *= arr[-(i + 1)]
return np.flip(stride)
def conv(
X,
W,
B=None,
auto_pad=None,
dilations=None,
group=None,
kernel_shape=None,
pads=None,
strides=None,
):
if dilations is None:
dilations = [1 for s in X.shape[2:]]
if kernel_shape is None:
kernel_shape = W.shape[2:]
if pads is None:
pads = [0 for s in X.shape[2:]] * 2
if strides is None:
strides = [1 for s in X.shape[2:]]
if X.shape[1] != W.shape[1] * group or W.shape[0] % group != 0:
raise ValueError(
f"Shape inconsistencies, X.shape={X.shape}, W.shape={W.shape}, group={group}, "
f"W should be {(W.shape[0], X.shape[1] // group, np.prod(W.shape[1:]) // X.shape[1] * group)}."
)
if group > 1:
res = []
td = 0
mg = W.shape[0] // group
dw = W.shape[1]
for b in range(X.shape[0]):
for g in range(group):
gx = X[b : b + 1, g * dw : (g + 1) * dw]
gw = W[g * mg : (g + 1) * mg]
try:
cv = conv(
gx,
gw,
None,
auto_pad,
dilations,
1,
kernel_shape,
pads,
strides,
)
except (ValueError, RuntimeError) as e:
raise ValueError(
f"Shape inconsistencies, X.shape={X.shape}, W.shape={W.shape}, group={g}/{group}, "
f"gx.shape={gx.shape}, gw.shape={gw.shape}, auto_pad={auto_pad}, "
f"dilations={dilations}, kernel_shape={kernel_shape}, pads={pads}, "
f"strides={strides}."
) from e
if b == 0:
td += cv.shape[1]
res.append((b, cv))
new_shape = [X.shape[0], *list(res[0][1].shape[1:])]
new_shape[1] = td
final = np.zeros(tuple(new_shape), dtype=res[0][1].dtype)
p = 0
for b, cv in res:
final[b : b + 1, p : p + cv.shape[1]] = cv
p += cv.shape[1]
if p >= final.shape[1]:
p = 0
if B is not None:
new_shape = [1 for s in final.shape]
new_shape[1] = B.shape[0]
b = B.reshape(tuple(new_shape))
final += b
return final
if dilations[0] != 1 or min(dilations) != max(dilations):
# Let's compute the dilated kernel.
nd = len(dilations)
new_kernel_shape = []
new_shape = list(W.shape[:-nd])
for i, d in enumerate(dilations):
di = len(W.shape) - nd + i
new_shape.append(W.shape[di] + (W.shape[di] - 1) * (d - 1))
new_kernel_shape.append(kernel_shape[i] + (kernel_shape[i] - 1) * (d - 1))
new_w = np.zeros(tuple(new_shape), dtype=W.dtype)
indices = [slice(0, new_w.shape[0]), slice(0, new_w.shape[1])]
for i, d in enumerate(dilations):
di = len(W.shape) - nd + i
indices.append(slice(0, new_w.shape[di], d))
new_w[tuple(indices)] = W
W = new_w
kernel_shape = new_kernel_shape
if auto_pad in {"SAME_LOWER", "SAME_UPPER", "VALID"}:
head = []
tail = []
for i in range(len(X.shape) - 2):
d = X.shape[i]
target_size = (d + strides[i] - 1) // strides[i]
pad_needed = (target_size - 1) * strides[i] + kernel_shape[i] - d
if auto_pad == "SAME_LOWER":
pad_head = (pad_needed + 1) // 2
else:
pad_head = pad_needed // 2
pad_tail = pad_needed - pad_head
head.append(pad_head)
tail.append(pad_tail)
pads = head + tail
if len(X.shape) == 3:
sN, sC, sH = X.shape
# M, C_group, kH, kW = W.shape
(kh,) = kernel_shape
(sth,) = strides
h_out = int(((sH - kh + pads[0] + pads[1]) / sth) + 1)
h0 = pads[0]
oh = -1 * (kh % 2)
bh = -h0
eh = h_out * sth
res = np.zeros((X.shape[0], W.shape[0], h_out)) # type: ignore[assignment]
if B is not None:
res[:, :, :] += B.reshape((1, -1, 1)) # type: ignore
for n in range(0, sN):
for nw in range(W.shape[0]):
for c in range(0, sC):
w = W[nw : nw + 1, c : c + 1]
for io in range(bh, eh, sth):
hr = (io - bh) // sth
if hr >= h_out:
continue
i = io + kh % 2
ih1, ih2 = max(0, i + oh), min(i + oh + kh, sH)
img = X[n : n + 1, c : c + 1, ih1:ih2]
if img.shape != w.shape:
jh1, jh2 = max(-oh - i, 0), min(kh, kh + sH - (i + oh + kh))
w_ = w[:1, :1, jh1:jh2]
if img.shape != w_.shape:
raise RuntimeError(
f"Unexpected shape {img.shape} != {w_.shape}, oh={oh}, "
f"i={i}, kh={kh}, sH={sH}, sth={sth}."
)
s = np.dot(img.reshape((1, -1)), w_.reshape((-1, 1)))[
0, 0
] # (img * w_).sum()
else:
s = np.dot(img.reshape((1, -1)), w.reshape((-1, 1)))[
0, 0
] # (img * w).sum()
res[n, nw, hr] += s # type: ignore
return res
if len(X.shape) == 4:
sN, sC, sH, sW = X.shape
# M, C_group, kH, kW = W.shape
kh, kw = kernel_shape
sth, stw = strides
h_out = int(((sH - kh + pads[0] + pads[2]) / sth) + 1)
w_out = int(((sW - kw + pads[1] + pads[3]) / stw) + 1)
h0, w0 = pads[0], pads[1]
oh, ow = -1 * (kh % 2), -1 * (kw % 2)
bh, bw = -h0, -w0
eh, ew = h_out * sth, w_out * stw
res = np.zeros((X.shape[0], W.shape[0], h_out, w_out)) # type: ignore[assignment]
if B is not None:
res[:, :, :, :] = B.reshape((1, -1, 1, 1)) # type: ignore
for n in range(0, sN):
for nw in range(W.shape[0]):
for c in range(0, sC):
w = W[nw : nw + 1, c : c + 1]
for io in range(bh, eh, sth):
hr = (io - bh) // sth
if hr >= h_out:
continue
i = io + kh % 2
ih1, ih2 = max(0, i + oh), min(i + oh + kh, sH)
for jo in range(bw, ew, stw):
wr = (jo - bw) // stw
if wr >= w_out:
continue
j = jo + kw % 2
iw1, iw2 = max(0, j + ow), min(j + ow + kw, sW)
img = X[n : n + 1, c : c + 1, ih1:ih2, iw1:iw2]
if img.shape != w.shape:
jh1, jh2 = max(-oh - i, 0), min(
kh, kh + sH - (i + oh + kh)
)
jw1, jw2 = max(-ow - j, 0), min(
kw, kw + sW - (j + ow + kw)
)
w_ = w[:1, :1, jh1:jh2, jw1:jw2]
if img.shape != w_.shape:
raise RuntimeError(
f"Unexpected shape {img.shape} != {w_.shape}, oh={oh}, ow={ow}, "
f"i={i}, j={j}, kh={kh}, kw={kw}, sH={sH}, sW={sW}, sth={sth}, stw={stw}."
)
s = np.dot(img.reshape((1, -1)), w_.reshape((-1, 1)))[
0, 0
] # (img * w_).sum()
else:
s = np.dot(img.reshape((1, -1)), w.reshape((-1, 1)))[
0, 0
] # (img * w).sum()
res[n, nw, hr, wr] += s # type: ignore
return res
if len(X.shape) == 5:
sN, sC, sH, sW, sZ = X.shape
kh, kw, kz = kernel_shape
sth, stw, stz = strides
h_out = int(((sH - kh + pads[0] + pads[3]) / sth) + 1)
w_out = int(((sW - kw + pads[1] + pads[4]) / stw) + 1)
z_out = int(((sZ - kz + pads[2] + pads[5]) / stz) + 1)
h0, w0, z0 = pads[0], pads[1], pads[2]
oh, ow, oz = -1 * (kh % 2), -1 * (kw % 2), -1 * (kz % 2)
bh, bw, bz = -h0, -w0, -z0
eh, ew, ez = h_out * sth, w_out * stw, z_out * stz
res = np.zeros((X.shape[0], W.shape[0], h_out, w_out, z_out)) # type: ignore[assignment]
if B is not None:
res[:, :, :, :, :] = B.reshape((1, -1, 1, 1, 1)) # type: ignore
for n in range(0, sN):
for nw in range(W.shape[0]):
for c in range(0, sC):
w = W[nw : nw + 1, c : c + 1]
for io in range(bh, eh, sth):
hr = (io - bh) // sth
if hr >= h_out:
continue
i = io + kh % 2
ih1, ih2 = max(0, i + oh), min(i + oh + kh, sH)
for jo in range(bw, ew, stw):
wr = (jo - bw) // stw
if wr >= w_out:
continue
j = jo + kw % 2
iw1, iw2 = max(0, j + ow), min(j + ow + kw, sW)
for zo in range(bz, ez, stz):
zr = (zo - bz) // stz
if zr >= z_out:
continue
z = zo + kz % 2
iz1, iz2 = max(0, z + oz), min(z + oz + kz, sZ)
img = X[n : n + 1, c : c + 1, ih1:ih2, iw1:iw2, iz1:iz2]
### ICI
if img.shape != w.shape:
jh1, jh2 = max(-oh - i, 0), min(
kh, kh + sH - (i + oh + kh)
)
jw1, jw2 = max(-ow - j, 0), min(
kw, kw + sW - (j + ow + kw)
)
jz1, jz2 = max(-oz - z, 0), min(
kz, kz + sZ - (z + oz + kz)
)
w_ = w[:1, :1, jh1:jh2, jw1:jw2, jz1:jz2]
if img.shape != w_.shape:
raise RuntimeError(
f"Unexpected shape {img.shape} != {w_.shape}, oh={oh}, ow={ow}, oz={oz}, "
f"i={i}, j={j}, z={z}, kh={kh}, kw={kw}, kz={kz}, "
f"sH={sH}, sW={sW}, sZ={sZ}, sth={sth}, stw={stw}, stz={stz}."
)
s = np.dot(
img.reshape((1, -1)), w_.reshape((-1, 1))
)[
0, 0
]
else:
s = np.dot(
img.reshape((1, -1)), w.reshape((-1, 1))
)[
0, 0
]
res[n, nw, hr, wr, zr] += s # type: ignore
return res
else:
nd = len(X.shape[2:])
sN, sC = X.shape[:2]
x_stride = stride(X.shape)
w_stride = stride(W.shape)
x_flatten = X.reshape(int(x_stride[0] * X.shape[0]))
shape_out = [int(((X.shape[2+i] - kernel_shape[i] + pads[i] + pads[i + nd]) / strides[i]) + 1) for i in range(nd)]
o_index = [-1 * (kernel_shape[i] % 2) for i in range(nd)]
b_index = [-pads[i] for i in range(nd)]
e_index = [shape_out[i] * strides[i] for i in range(nd)]
range_len = [e_index[i] - b_index[i] / strides[i] for i in range(nd)]
range_stride = stride(range_len)
res_shape = [X.shape[0], W.shape[0]] + shape_out
res = np.zeros(res_shape)
res_strides = stride(res_shape)
if B is not None:
res[:, :, :, :, :] = B.reshape((1, -1, 1, 1, 1)) # type: ignore
for n in range(0, sN):
for nw in range(W.shape[0]):
for c in range(0, sC):
w = W[nw : nw + 1, c : c + 1]
for i in range(int(range_len[0] * range_stride[0])):
flatten_index = i
io_index = np.zeros(nd)
r_index = np.zeros(nd)
for nx in range(nd):
n_index, rem = divmod(flatten_index, range_stride[nx])
flatten_index = rem
io_index[nx] = n_index * strides[nx] + b_index[nx]
r_index[nx] = n_index
if r_index_check(r_index, shape_out):
indices = [io_index[nx] + (kernel_shape[nx] % 2) for nx in range(nd)]
i1_index = [max(0, indices[nx] + o_index[nx]) for nx in range(nd)]
i2_index = [min(X.shape[2 + nx], indices[nx] + o_index[nx] + kernel_shape[nx]) for nx in range(nd)]
idiff_index = [int(i2_index[nx] - i1_index[nx]) for nx in range(nd - 1)]
i_stride = stride(idiff_index)
img = []
for ii in range(int(i_stride[0] * idiff_index[0])):
flatten_index = ii
start = n * x_stride[0] + c * x_stride[1]
for nx in range(nd - 1):
ii_index, rem = divmod(flatten_index, i_stride[nx])
flatten_index = rem
start += (i1_index[nx] + ii_index) * x_stride[2 + nx]
start += i1_index[nd-1]
end = start + (i2_index[nd-1] - i1_index[nd-1])
img.append(x_flatten[int(start):int(end)])
img_shape = [1, 1] + idiff_index
w = w.reshape(np.prod(kernel_shape))
if len(img) != len(w):
j1_index = [max(0, -indices[nx] - o_index[nx]) for nx in range(nd)]
j2_index = [min(X.shape[2 + nx] - indices[nx] - o_index[nx], kernel_shape[nx]) for nx in range(nd)]
jdiff_index = [j2_index[nx] - j1_index[nx] for nx in range(nd - 1)]
w_ = []
j_stride = stride(jdiff_index)
for jj in range(int(j_stride[0] * jdiff_index[0])):
flatten_index = jj
start = 0
for nx in range(nd):
jj_index, rem = divmod(flatten_index, range_stride[nx])
flatten_index = rem
start += (j1_index[nx] + jj_index) * kernel_shape[nx]
w_.append(w[int(start + j1_index[-1]):int(start + j1_index[-1] + j2_index[nd-1] - j1_index[nd-1])])
img = np.array(img)
s = np.dot(
np.array(img).reshape((1, -1)), np.array(w_).reshape((-1, 1))
)[
0, 0
]
else:
img = np.array(img)
s = np.dot(
np.array(img).reshape((1, -1)), np.array(w_).reshape((-1, 1))
)[
0, 0
]
res_index = []
for nx in range(nd):
res_index.append(int(r_index[nx]))
index = tuple([n, nw]) + tuple(res_index)
res[index] += s # type: ignore
return res
class Conv(RunAll):
@staticmethod
def export_conv_1D_no_padding() -> None:
x = np.array(
[
[
[
0.0, 1.0, 2.0, 3.0, 4.0
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
1.0, 1.0, 1.0
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_1D_no_padding"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_conv_1D_with_padding() -> None:
x = np.array(
[
[
[
0.0, 1.0, 2.0, 3.0, 4.0
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
1.0, 1.0, 1.0
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1, pads=[1, 1])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_1D_with_padding"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![1, 1].span()),"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_conv_2D_no_padding() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1, kernel_shape=[3, 3],pads=[0, 0, 0, 0],)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_2D_with_padding"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_con_2D_with_padding() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1, kernel_shape=[3, 3],pads=[1, 1, 1, 1],)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_2D_with_padding"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![1, 1, 1, 1].span()),"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_conv_3D_no_padding() -> None:
x = np.array(
[
[
[
[
[ 0, 1, 2, 3, 4],[ 5, 6, 7, 8, 9],[ 10, 11, 12, 13, 14],[ 15, 16, 17, 18, 19],[ 20, 21, 22, 23, 24]
],
[
[ 25, 26, 27, 28, 29],[ 30, 31, 32, 33, 34],[ 35, 36, 37, 38, 39],[ 40, 41, 42, 43, 44],[ 45, 46, 47, 48, 49]
],
[
[ 50, 51, 52, 53, 54],[ 55, 56, 57, 58, 59],[ 60, 61, 62, 63, 64],[ 65, 66, 67, 68, 69],[ 70, 71, 72, 73, 74]
],
[
[ 75, 76, 77, 78, 79],[ 80, 81, 82, 83, 84],[ 85, 86, 87, 88, 89],[ 90, 91, 92, 93, 94],[ 95, 96, 97, 98, 99]
],
[
[100, 101, 102, 103, 104],[105, 106, 107, 108, 109],[110, 111, 112, 113, 114],[115, 116, 117, 118, 119],[120, 121, 122, 123, 124]
]
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
[
[1., 1., 1.],[1., 1., 1.],[1., 1., 1.]
],
[
[1., 1., 1.],[1., 1., 1.],[1., 1., 1.]
],
[
[1., 1., 1.],[1., 1., 1.],[1., 1., 1.]
]
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_3D_no_padding"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_conv_3D_with_padding() -> None:
x = np.array(
[
[
[
[
[ 0, 1, 2, 3, 4],[ 5, 6, 7, 8, 9],[ 10, 11, 12, 13, 14],[ 15, 16, 17, 18, 19],[ 20, 21, 22, 23, 24]
],
[
[ 25, 26, 27, 28, 29],[ 30, 31, 32, 33, 34],[ 35, 36, 37, 38, 39],[ 40, 41, 42, 43, 44],[ 45, 46, 47, 48, 49]
],
[
[ 50, 51, 52, 53, 54],[ 55, 56, 57, 58, 59],[ 60, 61, 62, 63, 64],[ 65, 66, 67, 68, 69],[ 70, 71, 72, 73, 74]
],
[
[ 75, 76, 77, 78, 79],[ 80, 81, 82, 83, 84],[ 85, 86, 87, 88, 89],[ 90, 91, 92, 93, 94],[ 95, 96, 97, 98, 99]
],
[
[100, 101, 102, 103, 104],[105, 106, 107, 108, 109],[110, 111, 112, 113, 114],[115, 116, 117, 118, 119],[120, 121, 122, 123, 124]
]
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
[
[1., 1., 1.],[1., 1., 1.],[1., 1., 1.]
],
[
[1., 1., 1.],[1., 1., 1.],[1., 1., 1.]
],
[
[1., 1., 1.],[1., 1., 1.],[1., 1., 1.]
]
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1, pads=[1, 1, 1, 1, 1, 1])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_3D_with_padding"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![1, 1, 1, 1, 1, 1].span()),"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_conv_4D_no_padding() -> None:
x = np.array(
[
[
[
[
[
[ 0, 1, 2],[ 3, 4, 5],[ 6, 7, 8]
],
[
[ 9, 10, 11],[12, 13, 14],[15, 16, 17]
],
[
[18, 19, 20],[21, 22, 23],[24, 25, 26]
]
],
[
[
[27, 28, 29],[30, 31, 32],[33, 34, 35]
],
[
[36, 37, 38],[39, 40, 41],[42, 43, 44]
],
[
[45, 46, 47],[48, 49, 50],[51, 52, 53]
]
],
[
[
[54, 55, 56],[57, 58, 59],[60, 61, 62]
],
[
[63, 64, 65],[66, 67, 68],[69, 70, 71]
],
[
[72, 73, 74],[75, 76, 77],[78, 79, 80]
]
]
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
[
[
[1., 1.],[1., 1.]
],
[
[1., 1.],[1., 1.]
]
],
[
[
[1., 1.],[1., 1.]
],
[
[1., 1.],[1., 1.]
]
]
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_4D_no_padding"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_conv_4D_with_padding() -> None:
x = np.array(
[
[
[
[
[
[ 0, 1, 2],[ 3, 4, 5],[ 6, 7, 8]
],
[
[ 9, 10, 11],[12, 13, 14],[15, 16, 17]
],
[
[18, 19, 20],[21, 22, 23],[24, 25, 26]
]
],
[
[
[27, 28, 29],[30, 31, 32],[33, 34, 35]
],
[
[36, 37, 38],[39, 40, 41],[42, 43, 44]
],
[
[45, 46, 47],[48, 49, 50],[51, 52, 53]
]
],
[
[
[54, 55, 56],[57, 58, 59],[60, 61, 62]
],
[
[63, 64, 65],[66, 67, 68],[69, 70, 71]
],
[
[72, 73, 74],[75, 76, 77],[78, 79, 80]
]
]
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
[
[
[1., 1.],[1., 1.]
],
[
[1., 1.],[1., 1.]
]
],
[
[
[1., 1.],[1., 1.]
],
[
[1., 1.],[1., 1.]
]
]
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1, pads=[1, 1, 1, 1, 1, 1, 1, 1])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16)) #
name = "conv_4D_with_padding"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![1, 1, 1, 1, 1, 1, 1, 1].span()),"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_conv_with_autopad_same() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1, kernel_shape=[3, 3],auto_pad="SAME_LOWER",strides = [2, 2])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_2D_with_autopad_same"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::Some(AUTO_PAD::SAME_LOWER),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![3, 3].span()),"
func_sig += "Option::None,"
func_sig += "Option::Some(array![2, 2].span()))"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_conv_with_strides_asymmetric_padding() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
[25.0, 26.0, 27.0, 28.0, 29.0],
[30.0, 31.0, 32.0, 33.0, 34.0],
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1, kernel_shape=[3, 3],pads=[1, 0, 1, 0],strides = [2, 2])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_2D_with_strides_asymmetric_padding"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![3, 3].span()),"
func_sig += "Option::Some(array![1, 0, 1, 0].span()),"
func_sig += "Option::Some(array![2, 2].span()))"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_conv_with_strides_with_padding() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0],
[25.0, 26.0, 27.0, 28.0, 29.0],
[30.0, 31.0, 32.0, 33.0, 34.0],
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
]
]
]
).astype(np.float32)
y = conv(x, w, group = 1, kernel_shape=[3, 3],pads=[1, 1, 1, 1],strides = [2, 2])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_2D_with_strides_with_padding"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![3, 3].span()),"
func_sig += "Option::Some(array![1, 1, 1, 1].span()),"
func_sig += "Option::Some(array![2, 2].span()))"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_conv_with_2_groups() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],
[
[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]]
]
]
).astype(np.float32)
w = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
],
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
y = conv(x, w, group = 2)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_2D_with_2_groups"
func_sig = "NNTrait::conv("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(2),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/conv_transpose.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def conv_transpose(
X,
W,
B=None,
auto_pad=None,
dilations=None,
group=None,
kernel_shape=None,
output_padding=None,
output_shape=None,
pads=None,
strides=None,
):
if dilations is None:
dilations = [1 for s in X.shape[2:]]
if kernel_shape is None:
kernel_shape = W.shape[2:]
if output_padding is None:
output_padding = [0 for s in X.shape[2:]] * 2
if strides is None:
strides = [1 for s in X.shape[2:]]
if pads is None and auto_pad not in {"SAME_UPPER", "SAME_LOWER"}:
pads = [0 for i in range(2 * len(strides))]
if pads is None:
if output_shape is None:
output_shape = [
X.shape[i + 2] * strides[i] for i in range(len(strides))
]
total_padding = [
strides[i] * (X.shape[i + 2] - 1)
+ output_padding[i]
+ ((kernel_shape[i] - 1) * dilations[i] + 1)
- output_shape[i]
for i in range(len(output_shape))
]
pads_1 = []
pads_2 = []
for i in range(len(output_shape)):
if auto_pad == "SAME_UPPER":
pads_1.append(total_padding[i] // 2)
pads_2.append(total_padding[i] - (total_padding[i] // 2))
else:
pads_1.append(total_padding[i] - (total_padding[i] // 2))
pads_2.append(total_padding[i] // 2)
pads = pads_1 + pads_2
n_dims = len(pads) // 2
else:
n_dims = len(X.shape) - 2
new_pads = np.array([(pads[i], pads[i + n_dims]) for i in range(n_dims)])
if output_shape is None:
output_shape = [
strides[i] * (X.shape[i + 2] - 1)
+ output_padding[i]
+ ((kernel_shape[i] - 1) * dilations[i] + 1)
- new_pads[i, :].sum()
for i in range(n_dims)
]
kernel_shape = W.shape[2:]
kernel_size = np.prod(kernel_shape)
num_output_channels = W.shape[1] * group
kernel_dim = num_output_channels // group * kernel_size
C = X.shape[1] # num_inputs_channels
m = kernel_dim # kernel_dim
n = np.prod(X.shape[2:]) # input_image_size
k = C // group
w_reshaped = W.reshape((group, k, m))
final = None
# N x C x H x W = X.shape
# C x M/group x k1 x k2 = W.shape
if group == 1:
for image_id in range(X.shape[0]):
w_t = w_reshaped[0].T
gemm = np.matmul(w_t, X[image_id].reshape((k, n)))
gemmc = gemm.reshape((num_output_channels, -1, gemm.shape[-1]))
for c in range(num_output_channels):
res = col2im_naive_implementation(
gemmc[c], output_shape, kernel_shape, dilations, pads, strides
)
if final is None:
final = np.empty(
X.shape[:1] + (num_output_channels,) + res.shape,
dtype=X.dtype,
)
if B is not None:
res += B[c]
final[image_id, c, ...] = res[...]
else:
final = np.zeros((X.shape[0], num_output_channels ) + tuple(output_shape))
output_array = []
for group_id in range(group):
group_X = X[:, group_id * C // group : (group_id + 1) * C // group, ...]
group_W = W[group_id * num_output_channels // group : (group_id + 1) * num_output_channels // group, ...]
group_output = conv_transpose(
group_X,
group_W,
B=B,
auto_pad=auto_pad,
dilations=dilations,
group=1,
kernel_shape=kernel_shape,
output_padding=output_padding,
output_shape=output_shape,
pads=pads,
strides=strides,
)
group_output = np.array(group_output[0])
output_array.append(group_output)
for image_id in range(X.shape[0]):
for group_id in range(group):
group_output = output_array[group_id]
final[image_id, group_id:(group_id+1), ...] = group_output[image_id, ...]
return (final.astype(X.dtype),)
def _get_indices(i, shape):
res = np.empty((len(shape),), dtype=np.int64)
k = len(shape) - 1
while k > 0:
m = i % shape[k]
res[k] = m
i -= m
i /= shape[k]
k -= 1
res[0] = i
return res
def _col2im_shape_check(X, output_shape, kernel_shape, dilations, pads, strides): # type: ignore
n_input_plane = X.shape[0]
kernel_size = np.prod(kernel_shape)
if n_input_plane % kernel_size != 0:
raise ValueError(
f"Expected size of input's dimension 1 to be divisible by the "
f"product of kernel_size={kernel_size}, "
f"but got input.size(1)={n_input_plane} "
f"and kernel_shape={kernel_shape}, X.shape={X.shape}, output_shape={output_shape}."
)
input_length = X.shape[1]
n_dims = len(output_shape)
n_blocks = []
for i in range(n_dims):
n_block = (
output_shape[i]
+ pads[i, :].sum()
- dilations[i] * (kernel_shape[i] - 1)
- 1
) // strides[i] + 1
n_blocks.append(n_block)
block_size = np.prod(n_blocks)
if input_length != block_size:
raise ValueError(
f"Given n_input_plane={n_input_plane}, X.shape={X.shape}, "
f"output_shape={output_shape}, kernel_shape={kernel_shape}, "
f"dilations={dilations}, pads={pads}, strides={strides}, "
f"expected size of input's dimension 2 to match the calculated number of "
f"sliding blocks {n_blocks} = {block_size}, "
f"but got input.size(2)={input_length}.",
)
def col2im_naive_implementation(data, image_shape, kernel_shape, dilations, pads, strides): # type: ignore
n_dims = len(pads) // 2
new_pads = np.array([(pads[i], pads[i + n_dims]) for i in range(n_dims)])
_col2im_shape_check(data, image_shape, kernel_shape, dilations, new_pads, strides)
data_col = data
data_im = np.zeros(image_shape, dtype=data.dtype)
dim_col = []
for i in range(n_dims):
col = (
image_shape[i]
+ new_pads[i, :].sum()
- (dilations[i] * (kernel_shape[i] - 1) + 1)
) // strides[i] + 1
dim_col.append(col)
kernel_size = np.prod(kernel_shape)
col_size = np.prod(dim_col)
for c_col in range(kernel_size):
offset = _get_indices(c_col, kernel_shape)
for col in range(col_size):
ind_col = _get_indices(col, dim_col)
ind_im = []
for i in range(n_dims):
ind = (
ind_col[i] * strides[i] - new_pads[i, 0] + offset[i] * dilations[i]
)
ind_im.append(ind)
if not _is_out(ind_im, data_im.shape):
data_im[tuple(ind_im)] += data_col[c_col, col]
return data_im
def _is_out(ind, shape):
for i, s in zip(ind, shape):
if i < 0:
return True
if i >= s:
return True
return False
class Conv_transpose(RunAll):
@staticmethod
def export_conv_transpose() -> None:
x = np.array(
[[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)
).astype(np.float32)
w = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
y = conv_transpose(x, w, group=1)[0]
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_transpose"
func_sig = "NNTrait::conv_transpose("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_convtranspose_1d() -> None:
x = np.array([[[0.0, 1.0, 2.0]]]).astype(np.float32) # (1, 1, 3)
w = np.array([[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]).astype( # (1, 2, 3)
np.float32
)
y = conv_transpose(x, w, group=1)[0]
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_transpose_1d"
func_sig = "NNTrait::conv_transpose("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_convtranspose_3d() -> None:
x = np.array(
[
[
[
[
[0.0, 1.0, 2.0, 3.0, 4.0], # (1, 1, 3, 4, 5)
[5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0],
[15.0, 16.0, 17.0, 18.0, 19.0],
],
[
[20.0, 21.0, 22.0, 23.0, 24.0],
[25.0, 26.0, 27.0, 28.0, 29.0],
[30.0, 31.0, 32.0, 33.0, 34.0],
[35.0, 36.0, 37.0, 38.0, 39.0],
],
[
[40.0, 41.0, 42.0, 43.0, 44.0],
[45.0, 46.0, 47.0, 48.0, 49.0],
[50.0, 51.0, 52.0, 53.0, 54.0],
[55.0, 56.0, 57.0, 58.0, 59.0],
],
]
]
]
).astype(np.float32)
w = np.array(
[
[
[
[
[1.0, 1.0, 1.0], # (1, 2, 3, 3, 3)
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
],
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
],
]
]
).astype(np.float32)
y = conv_transpose(x, w, group=1)[0]
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_transpose_3d"
func_sig = "NNTrait::conv_transpose("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_convtranspose_attributes() -> None:
x = np.array(
[[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)
).astype(np.float32)
w = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
y = conv_transpose(x, w, group=1)[0]
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_transpose_attributes"
func_sig = "NNTrait::conv_transpose("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_convtranspose_pads() -> None:
x = np.array(
[[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)
).astype(np.float32)
w = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
y = conv_transpose(x, w, group=1,strides=[3, 2],output_shape=[10, 8], kernel_shape=[3, 3], output_padding=[1, 1],)[0]
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_transpose_pads"
func_sig = "NNTrait::conv_transpose("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![3, 3].span()),"
func_sig += "Option::Some(array![1, 1].span()),"
func_sig += "Option::Some(array![10, 8].span()),"
func_sig += "Option::None,"
func_sig += "Option::Some(array![3, 2].span()))"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_convtranspose_dilations() -> None:
x = np.array(
[[[[3.0, 8.0, 1.0], [9.0, 5.0, 7.0], [3.0, 2.0, 6.0]]]] # (1, 1, 3, 3)
).astype(np.float32)
w = np.array([[[[7.0, 2.0], [1.0, 9.0]]]]).astype(np.float32) # (1, 1, 2, 2)
y = conv_transpose(x, w, group=1, dilations=[2, 2])[0]
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_transpose_dilations"
func_sig = "NNTrait::conv_transpose("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![2, 2].span()),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_convtranspose_autopad_same() -> None:
x = np.array(
[[[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]]] # (1, 1, 3, 3)
).astype(np.float32)
w = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], # (1, 2, 3, 3)
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
y = conv_transpose(x, w, group=1, auto_pad="SAME_UPPER", strides=[2, 2])[0]
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_transpose_autopad_same"
func_sig = "NNTrait::conv_transpose("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::Some(AUTO_PAD::SAME_UPPER),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(array![2, 2].span()))"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_convtranspose_group_2() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],
[
[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]]
]
]
).astype(np.float32)
w = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
],
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
y = conv_transpose(x, w, group=2)[0]
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_transpose_group_2"
func_sig = "NNTrait::conv_transpose("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(2),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
@staticmethod
def export_convtranspose_group_2_image_3() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]],
[
[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]
]
],
[
[
[18.0, 19.0, 20.0], [21.0, 22.0, 23.0], [24.0, 25.0, 26.0]
],
[
[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]
]
],
[
[
[0.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]
],
[
[9.0, 10.0, 11.0], [12.0, 13.0, 14.0], [15.0, 16.0, 17.0]
]
]
]
).astype(np.float32)
w = np.array(
[
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
],
[
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
]
).astype(np.float32)
y = conv_transpose(x, w, group=2)[0]
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(w.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "conv_transpose_group_2_image_3"
func_sig = "NNTrait::conv_transpose("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(2),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,)"
make_test(
[x, w], y, func_sig, name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/cos.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Cos(RunAll):
@staticmethod
def cos_fp8x23():
x = np.random.uniform(-10, 127, (2, 2)).astype(np.float64)
y = np.cos(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "cos_fp8x23"
make_test([x], y, "input_0.cos()", name)
@staticmethod
def cos_fp16x16():
x = np.random.uniform(-10, 127, (2, 2)).astype(np.float64)
y = np.cos(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "cos_fp16x16"
make_test([x], y, "input_0.cos()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/cosh.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Cosh(RunAll):
@staticmethod
def cosh_fp8x23():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = np.cosh(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "cosh_fp8x23"
make_test([x], y, "input_0.cosh()", name)
@staticmethod
def cosh_fp16x16():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = np.cosh(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "cosh_fp16x16"
make_test([x], y, "input_0.cosh()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/cumsum.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Cumsum(RunAll):
@staticmethod
def cumsum_u32():
def cumsum_1D():
def default():
x = np.array([1, 2, 3, 4, 5]).astype(np.uint32)
y = np.array([1, 3, 6, 10, 15]).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "cumsum_u32_1d_default"
make_test(
[x], y, "input_0.cumsum(0, Option::None(()), Option::None(()))", name)
def exclusive():
x = np.array([1, 2, 3, 4, 5]).astype(np.uint32)
y = np.array([0, 1, 3, 6, 10]).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "cumsum_u32_1d_exclusive"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(true), Option::Some(false))", name)
def reverse():
x = np.array([1, 2, 3, 4, 5]).astype(np.uint32)
y = np.array([15, 14, 12, 9, 5]).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "cumsum_u32_1d_reverse"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(false), Option::Some(true))", name)
def reverse_exclusive():
x = np.array([1, 2, 3, 4, 5]).astype(np.uint32)
y = np.array([14, 12, 9, 5, 0]).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "cumsum_u32_1d_reverse_exclusive"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(true), Option::Some(true))", name)
default()
exclusive()
reverse()
reverse_exclusive()
cumsum_1D()
def cumsum_2D():
def axis_0():
x = np.array([1, 2, 3, 4, 5, 6]).astype(
np.uint32).reshape((2, 3))
y = np.array([1, 2, 3, 5, 7, 9]).astype(
np.uint32).reshape((2, 3))
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "cumsum_u32_2d_axis_0"
make_test(
[x], y, "input_0.cumsum(0, Option::None(()), Option::None(()))", name)
def axis_1():
x = np.array([1, 2, 3, 4, 5, 6]).astype(
np.uint32).reshape((2, 3))
y = np.array([1, 3, 6, 4, 9, 15]).astype(
np.uint32).reshape((2, 3))
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "cumsum_u32_2d_axis_1"
make_test(
[x], y, "input_0.cumsum(1, Option::None(()), Option::None(()))", name)
axis_0()
axis_1()
cumsum_2D()
@staticmethod
def cumsum_i32():
def cumsum_1D():
def default():
x = np.array([1, 2, 3, 4, 5]).astype(np.int32)
y = np.array([1, 3, 6, 10, 15]).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "cumsum_i32_1d_default"
make_test(
[x], y, "input_0.cumsum(0, Option::None(()), Option::None(()))", name)
def exclusive():
x = np.array([1, 2, 3, 4, 5]).astype(np.int32)
y = np.array([0, 1, 3, 6, 10]).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "cumsum_i32_1d_exclusive"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(true), Option::Some(false))", name)
def reverse():
x = np.array([1, 2, 3, 4, 5]).astype(np.int32)
y = np.array([15, 14, 12, 9, 5]).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "cumsum_i32_1d_reverse"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(false), Option::Some(true))", name)
def reverse_exclusive():
x = np.array([1, 2, 3, 4, 5]).astype(np.int32)
y = np.array([14, 12, 9, 5, 0]).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "cumsum_i32_1d_reverse_exclusive"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(true), Option::Some(true))", name)
default()
exclusive()
reverse()
reverse_exclusive()
cumsum_1D()
def cumsum_2D():
def axis_0():
x = np.array([1, 2, 3, 4, 5, 6]).astype(
np.int32).reshape((2, 3))
y = np.array([1, 2, 3, 5, 7, 9]).astype(
np.int32).reshape((2, 3))
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "cumsum_i32_2d_axis_0"
make_test(
[x], y, "input_0.cumsum(0, Option::None(()), Option::None(()))", name)
def axis_1():
x = np.array([1, 2, 3, 4, 5, 6]).astype(
np.int32).reshape((2, 3))
y = np.array([1, 3, 6, 4, 9, 15]).astype(
np.int32).reshape((2, 3))
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "cumsum_i32_2d_axis_1"
make_test(
[x], y, "input_0.cumsum(1, Option::None(()), Option::None(()))", name)
axis_0()
axis_1()
cumsum_2D()
@staticmethod
def cumsum_i8():
def cumsum_1D():
def default():
x = np.array([1, 2, 3, 4, 5]).astype(np.int8)
y = np.array([1, 3, 6, 10, 15]).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "cumsum_i8_1d_default"
make_test(
[x], y, "input_0.cumsum(0, Option::None(()), Option::None(()))", name)
def exclusive():
x = np.array([1, 2, 3, 4, 5]).astype(np.int8)
y = np.array([0, 1, 3, 6, 10]).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "cumsum_i8_1d_exclusive"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(true), Option::Some(false))", name)
def reverse():
x = np.array([1, 2, 3, 4, 5]).astype(np.int8)
y = np.array([15, 14, 12, 9, 5]).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "cumsum_i8_1d_reverse"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(false), Option::Some(true))", name)
def reverse_exclusive():
x = np.array([1, 2, 3, 4, 5]).astype(np.int8)
y = np.array([14, 12, 9, 5, 0]).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "cumsum_i8_1d_reverse_exclusive"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(true), Option::Some(true))", name)
default()
exclusive()
reverse()
reverse_exclusive()
cumsum_1D()
def cumsum_2D():
def axis_0():
x = np.array([1, 2, 3, 4, 5, 6]).astype(
np.int8).reshape((2, 3))
y = np.array([1, 2, 3, 5, 7, 9]).astype(
np.int8).reshape((2, 3))
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "cumsum_i8_2d_axis_0"
make_test(
[x], y, "input_0.cumsum(0, Option::None(()), Option::None(()))", name)
def axis_1():
x = np.array([1, 2, 3, 4, 5, 6]).astype(
np.int8).reshape((2, 3))
y = np.array([1, 3, 6, 4, 9, 15]).astype(
np.int8).reshape((2, 3))
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "cumsum_i8_2d_axis_1"
make_test(
[x], y, "input_0.cumsum(1, Option::None(()), Option::None(()))", name)
axis_0()
axis_1()
cumsum_2D()
@staticmethod
def cumsum_fp8x23():
def cumsum_1D():
def default():
x = np.array([1, 2, 3, 4, 5]).astype(np.int64)
y = np.array([1, 3, 6, 10, 15]).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "cumsum_fp8x23_1d_default"
make_test(
[x], y, "input_0.cumsum(0, Option::None(()), Option::None(()))", name)
def exclusive():
x = np.array([1, 2, 3, 4, 5]).astype(np.int64)
y = np.array([0, 1, 3, 6, 10]).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "cumsum_fp8x23_1d_exclusive"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(true), Option::Some(false))", name)
def reverse():
x = np.array([1, 2, 3, 4, 5]).astype(np.int64)
y = np.array([15, 14, 12, 9, 5]).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "cumsum_fp8x23_1d_reverse"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(false), Option::Some(true))", name)
def reverse_exclusive():
x = np.array([1, 2, 3, 4, 5]).astype(np.int64)
y = np.array([14, 12, 9, 5, 0]).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "cumsum_fp8x23_1d_reverse_exclusive"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(true), Option::Some(true))", name)
default()
exclusive()
reverse()
reverse_exclusive()
cumsum_1D()
def cumsum_2D():
def axis_0():
x = np.array([1, 2, 3, 4, 5, 6]).astype(
np.int64).reshape((2, 3))
y = np.array([1, 2, 3, 5, 7, 9]).astype(
np.int64).reshape((2, 3))
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "cumsum_fp8x23_2d_axis_0"
make_test(
[x], y, "input_0.cumsum(0, Option::None(()), Option::None(()))", name)
def axis_1():
x = np.array([1, 2, 3, 4, 5, 6]).astype(
np.int64).reshape((2, 3))
y = np.array([1, 3, 6, 4, 9, 15]).astype(
np.int64).reshape((2, 3))
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "cumsum_fp8x23_2d_axis_1"
make_test(
[x], y, "input_0.cumsum(1, Option::None(()), Option::None(()))", name)
axis_0()
axis_1()
cumsum_2D()
@staticmethod
def cumsum_fp16x16():
def cumsum_1D():
def default():
x = np.array([1, 2, 3, 4, 5]).astype(np.int64)
y = np.array([1, 3, 6, 10, 15]).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "cumsum_fp16x16_1d_default"
make_test(
[x], y, "input_0.cumsum(0, Option::None(()), Option::None(()))", name)
def exclusive():
x = np.array([1, 2, 3, 4, 5]).astype(np.int64)
y = np.array([0, 1, 3, 6, 10]).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "cumsum_fp16x16_1d_exclusive"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(true), Option::Some(false))", name)
def reverse():
x = np.array([1, 2, 3, 4, 5]).astype(np.int64)
y = np.array([15, 14, 12, 9, 5]).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "cumsum_fp16x16_1d_reverse"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(false), Option::Some(true))", name)
def reverse_exclusive():
x = np.array([1, 2, 3, 4, 5]).astype(np.int64)
y = np.array([14, 12, 9, 5, 0]).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "cumsum_fp16x16_1d_reverse_exclusive"
make_test(
[x], y, "input_0.cumsum(0, Option::Some(true), Option::Some(true))", name)
default()
exclusive()
reverse()
reverse_exclusive()
cumsum_1D()
def cumsum_2D():
def axis_0():
x = np.array([1, 2, 3, 4, 5, 6]).astype(
np.int64).reshape((2, 3))
y = np.array([1, 2, 3, 5, 7, 9]).astype(
np.int64).reshape((2, 3))
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "cumsum_fp16x16_2d_axis_0"
make_test(
[x], y, "input_0.cumsum(0, Option::None(()), Option::None(()))", name)
def axis_1():
x = np.array([1, 2, 3, 4, 5, 6]).astype(
np.int64).reshape((2, 3))
y = np.array([1, 3, 6, 4, 9, 15]).astype(
np.int64).reshape((2, 3))
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "cumsum_fp16x16_2d_axis_1"
make_test(
[x], y, "input_0.cumsum(1, Option::None(()), Option::None(()))", name)
axis_0()
axis_1()
cumsum_2D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/depth_to_space.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def depth_to_space(data: np.ndarray, blocksize: int = 2, mode = "DCR") -> np.ndarray:
if len(data.shape) != 4:
raise RuntimeError(f"Unexpected shape {data.shape!r}.")
b, c, h, w = data.shape
if mode == "DCR":
tmpshape = (
b,
blocksize,
blocksize,
c // (blocksize * blocksize),
h,
w,
)
reshaped = data.reshape(tmpshape)
transposed = np.transpose(reshaped, [0, 3, 4, 1, 5, 2])
else:
# assert mode == "CRD"
tmpshape = (
b,
c // (blocksize * blocksize),
blocksize,
blocksize,
h,
w,
)
reshaped = data.reshape(tmpshape)
transposed = np.transpose(reshaped, [0, 1, 4, 2, 5, 3])
finalshape = (
b,
c // (blocksize * blocksize),
h * blocksize,
w * blocksize,
)
y = np.reshape(transposed, finalshape)
return y
class Depth_to_space(RunAll):
@staticmethod
def fp8x23():
x = np.random.uniform(-3, 3, (1, 4, 2, 2)).astype(np.float64)
y = depth_to_space(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "depth_to_space_fp8x23"
make_test([x], y, "NNTrait::depth_to_space(@input_0, 2, 'DCR')",
name, Trait.NN)
@staticmethod
def fp16x16():
x = np.random.uniform(-3, 3, (1, 4, 2, 2)).astype(np.float16)
y = depth_to_space(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "depth_to_space_fp16x16"
make_test([x], y, "NNTrait::depth_to_space(@input_0, 2, 'DCR')",
name, Trait.NN)
# @staticmethod
# def fp64x64():
# x = np.random.uniform(-3, 3, (1, 4, 2, 2)).astype(np.float64)
# y = depth_to_space(x)
# x = Tensor(Dtype.FP64x64, x.shape, to_fp(
# x.flatten(), FixedImpl.FP64x64))
# y = Tensor(Dtype.FP64x64, y.shape, to_fp(
# y.flatten(), FixedImpl.FP64x64))
# name = "depth_to_space_fp64x64"
# make_test([x], y, "NNTrait::depth_to_space(@input_0, 2, 'DCR')",
# name, Trait.NN)
@staticmethod
def fpi8():
x = np.random.randint(-3, 3, (1, 4, 2, 2)).astype(np.int8)
y = depth_to_space(x)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "depth_to_space_i8"
make_test([x], y, "NNTrait::depth_to_space(@input_0, 2, 'DCR')",
name, Trait.NN)
@staticmethod
def fpi32():
x = np.random.randint(-3, 3, (1, 4, 2, 2)).astype(np.int32)
y = depth_to_space(x)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "depth_to_space_i32"
make_test([x], y, "NNTrait::depth_to_space(@input_0, 2, 'CRD')",
name, Trait.NN)
@staticmethod
def fpu32():
x = np.random.randint(-3, 3, (1, 4, 2, 2)).astype(np.uint32)
y = depth_to_space(x)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "depth_to_space_u32"
make_test([x], y, "NNTrait::depth_to_space(@input_0, 2, 'CRD')",
name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/div.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Div(RunAll):
@staticmethod
def div_u32():
def default():
x = np.random.randint(3, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(1, 3, (3, 3, 3)).astype(np.uint32)
z = x / y
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "div_u32"
make_test([x, y], z, "input_0 / input_1", name)
def broadcast():
x = np.random.randint(3, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(1, 3, (1, 3, 1)).astype(np.uint32)
z = x / y
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "div_u32_broadcast"
make_test([x, y], z, "input_0 / input_1", name)
default()
broadcast()
@staticmethod
def div_i32():
def default():
x = np.random.randint(1, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(1, 3, (3, 3, 3)).astype(np.int32)
z = x / y
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "div_i32"
make_test([x, y], z, "input_0 / input_1", name)
def broadcast():
x = np.random.randint(1, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(1, 3, (1, 3, 1)).astype(np.int32)
z = x / y
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "div_i32_broadcast"
make_test([x, y], z, "input_0 / input_1", name)
default()
broadcast()
@staticmethod
def div_i8():
def default():
x = np.random.randint(1, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(1, 3, (3, 3, 3)).astype(np.int8)
z = x / y
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name = "div_i8"
make_test([x, y], z, "input_0 / input_1", name)
def broadcast():
x = np.random.randint(1, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(1, 3, (1, 3, 1)).astype(np.int8)
z = x / y
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name = "div_i8_broadcast"
make_test([x, y], z, "input_0 / input_1", name)
default()
broadcast()
@staticmethod
def div_fp8x23():
def default():
x = np.random.randint(1, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(1, 3, (3, 3, 3)).astype(np.float64)
z = x / y
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "div_fp8x23"
make_test([x, y], z, "input_0 / input_1", name)
def broadcast():
x = np.random.randint(1, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(1, 3, (1, 3, 1)).astype(np.float64)
z = x / y
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "div_fp8x23_broadcast"
make_test([x, y], z, "input_0 / input_1", name)
default()
broadcast()
@staticmethod
def div_fp16x16():
def default():
x = np.random.randint(1, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(1, 3, (3, 3, 3)).astype(np.float64)
z = x / y
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "div_fp16x16"
make_test([x, y], z, "input_0 / input_1", name)
def broadcast():
x = np.random.randint(1, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(1, 3, (1, 3, 1)).astype(np.float64)
z = x / y
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "div_fp16x16_broadcast"
make_test([x, y], z, "input_0 / input_1", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/equal.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Equal(RunAll):
@staticmethod
def equal_u32():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.equal(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "equal_u32"
make_test([x, y], z, "input_0.equal(@input_1)", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.uint32)
y = np.random.randint(0, 6, (1, 2)).astype(np.uint32)
z = np.equal(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "equal_u32_broadcast"
make_test([x, y], z, "input_0.equal(@input_1)", name)
default()
broadcast()
@staticmethod
def equal_i32():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
z = np.equal(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "equal_i32"
make_test([x, y], z, "input_0.equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int32)
y = np.random.randint(-3, 3, (1, 2)).astype(np.int32)
z = np.equal(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "equal_i32_broadcast"
make_test([x, y], z, "input_0.equal(@input_1)", name)
default()
broadcast()
@staticmethod
def equal_i8():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
z = np.equal(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "equal_i8"
make_test([x, y], z, "input_0.equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int8)
y = np.random.randint(-3, 3, (1, 2)).astype(np.int8)
z = np.equal(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "equal_i8_broadcast"
make_test([x, y], z, "input_0.equal(@input_1)", name)
default()
broadcast()
@staticmethod
def equal_fp8x23():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.equal(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "equal_fp8x23"
make_test([x, y], z, "input_0.equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.equal(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "equal_fp8x23_broadcast"
make_test([x, y], z, "input_0.equal(@input_1)", name)
default()
broadcast()
@staticmethod
def equal_fp16x16():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.equal(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "equal_fp16x16"
make_test([x, y], z, "input_0.equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.equal(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "equal_fp16x16_broadcast"
make_test([x, y], z, "input_0.equal(@input_1)", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/erf.py
|
import numpy as np
from math import erf
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Erf(RunAll):
@staticmethod
def erf_fp8x23():
x = np.asarray([0.12, -1.66, 3.4, 4.8, 2.7]).astype(np.float64).reshape(1,5)
y = np.asarray([erf(value) for value in x[0]]).astype(np.float64).reshape(1,5)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "erf_fp8x23"
make_test([x], y, "input_0.erf()", name)
@staticmethod
def erf_fp16x16():
x = np.asarray([0.12, -1.66, 3.4, 4.8, 2.7]).astype(np.float64).reshape(1,5)
y = np.asarray([erf(value) for value in x[0]]).astype(np.float64).reshape(1,5)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "erf_fp16x16"
make_test([x], y, "input_0.erf()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/exp.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Exp(RunAll):
@staticmethod
def exp_fp8x23():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = np.exp(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "exp_fp8x23"
make_test([x], y, "input_0.exp()", name)
@staticmethod
def exp_fp16x16():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = np.exp(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "exp_fp16x16"
make_test([x], y, "input_0.exp()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/gather.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
class Gather(RunAll):
@staticmethod
def gather_fp16x16():
def default():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.array([[0,1], [2,1], [0, 2]]).astype(np.uint32)
y = x1.take(x2, axis=0)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_fp16x16_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather(indices:input_1, axis:Option::Some(0))",
name= name)
def axis1():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.array([[0,1], [2,1], [0, 2]]).astype(np.int64)
y = x1.take(x2, axis=1)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_fp16x16_3d_axis1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather(indices:input_1, axis:Option::Some(1))",
name= name)
def axis2():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.array([[0,1], [2,1], [0, 2]]).astype(np.int64)
y = x1.take(x2, axis=2)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_fp16x16_3d_axis2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather(indices:input_1, axis:Option::Some(2))",
name= name)
def negative_indices():
x1 = np.arange(10).astype(np.float32)
x2 = np.array([0, -9, -10]).astype(np.int64)
y = np.take(x1, x2, axis=0)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_negative_indices"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather(indices:input_1, axis:Option::Some(0))",
name= name)
def negative_axis():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.array([[0,1], [2,1], [0, 2]]).astype(np.uint32)
y = x1.take(x2, axis=-1)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_negative_axis"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather(indices:input_1, axis:Option::Some(-1))",
name= name)
default()
axis1()
axis2()
negative_indices()
negative_axis()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/gather_elements.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def gather_elements(data, indices, axis=0): # type: ignore
data_swaped = np.swapaxes(data, 0, axis)
index_swaped = np.swapaxes(indices, 0, axis)
gathered = np.choose(index_swaped, data_swaped, mode="wrap")
y = np.swapaxes(gathered, 0, axis)
return y
class Gather_elements(RunAll):
@staticmethod
def gather_elements_fp16x16():
def default():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.random.randint(low = 0,high=2, size=(3,3,3)).astype(np.uint32)
y = gather_elements(x1, x2, axis=0)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_elements_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_elements(indices:input_1, axis:Option::Some(0))",
name= name)
def axis1():
x1 = np.array([[1, 2], [3, 4]], dtype=np.float32)
x2 = np.array([[0, 0], [1, 0]], dtype=np.int32)
y = gather_elements(x1, x2, axis=1)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_elements_axis1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_elements(indices:input_1, axis:Option::Some(1))",
name= name)
def axis2():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.random.randint(low = 0,high=3, size=(3,3,3)).astype(np.uint32)
y = gather_elements(x1, x2, axis=2)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_elements_axis2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_elements(indices:input_1, axis:Option::Some(2))",
name= name)
def negative_indices():
x1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
x2 = np.array([[-1, -2, 0], [-2, 0, 0]], dtype=np.int32)
y = gather_elements(x1, x2, axis=0)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_elements_negative_indices"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_elements(indices:input_1, axis:Option::Some(0))",
name= name)
default()
axis1()
axis2()
negative_indices()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/gather_nd.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
def gather_nd_impl(
data: np.ndarray, indices: np.ndarray, batch_dims: int
) -> np.ndarray:
# Note the data rank - will be reused multiple times later
data_rank = len(data.shape)
# Check input tensors' shape/rank condition
assert indices.shape[-1] <= data_rank
# The list of data/indice shape of batch_dims
batch_dims_shape = []
# The number of elements in the batch_dims for data/indice array
batch_dims_size = 1
# Check the shape of indice and data are identicial for batch dims.
for i in range(batch_dims):
batch_dims_shape.append(indices.shape[i])
batch_dims_size *= indices.shape[i]
# Compute output of the op as below
# Compute shape of output array
output_shape = (
batch_dims_shape + list(indices.shape)[batch_dims:-1]
if (indices.shape[-1] == data_rank - batch_dims)
else batch_dims_shape
+ list(indices.shape)[batch_dims:-1]
+ list(data.shape)[batch_dims + indices.shape[-1] :]
)
# Placeholder for output data
output_data_buffer = []
# Flatten 'indices' to 2D array
reshaped_indices = indices.reshape(batch_dims_size, -1, indices.shape[-1])
# Flatten 'data' to array of shape (batch_dim_size, data.shape[batch_dimes:])
reshaped_data = data.reshape((batch_dims_size,) + data.shape[batch_dims:])
# gather each scalar value from 'data'
for batch_dim in range(reshaped_indices.shape[0]):
for outer_dim in range(reshaped_indices.shape[1]):
gather_index = tuple(reshaped_indices[batch_dim][outer_dim])
output_data_buffer.append(reshaped_data[(batch_dim, *gather_index)])
return np.asarray(output_data_buffer, dtype=data.dtype).reshape(output_shape)
class Gather_nd(RunAll):
@staticmethod
def gather_nd_fp16x16():
def gather_nd_3D():
def default():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.random.randint(low = 0,high=2, size=(3,3,3)).astype(np.uint32)
y = gather_nd_impl(x1, x2, batch_dims=0)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_nd_fp16x16_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(0))",
name= name)
def batch_dims1():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.random.randint(low = 0,high=2, size=(3,3,2)).astype(np.uint32)
y = gather_nd_impl(x1, x2, batch_dims=1)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_nd_fp16x16_3d_batch_dims1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(1))",
name= name)
def batch_dims2():
x1 = np.arange(0,54).reshape(3,3,3,2).astype(np.int64)
x2 = np.random.randint(low = 0,high=2, size=(3,3,2)).astype(np.uint32)
y = gather_nd_impl(x1, x2, batch_dims=2)
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gather_nd_fp16x16_3d_batch_dims2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(2))",
name= name)
default()
batch_dims1()
batch_dims2()
gather_nd_3D()
@staticmethod
def gather_nd_fp8x23():
def gather_nd_3D():
def default():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.random.randint(low = 0,high=2, size=(3,3,3)).astype(np.int64)
y = gather_nd_impl(x1, x2, batch_dims=0)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "gather_nd_fp8x23_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(0))",
name= name)
def batch_dims1():
x1 = np.arange(0,27).reshape(3,3,3).astype(np.int64)
x2 = np.random.randint(low = 0,high=2, size=(3,3,2)).astype(np.int64)
y = gather_nd_impl(x1, x2, batch_dims=1)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "gather_nd_fp8x23_3d_batch_dims1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(1))",
name= name)
def batch_dims2():
x1 = np.arange(0,54).reshape(3,3,3,2).astype(np.int64)
x2 = np.random.randint(low = 0,high=2, size=(3,3,2)).astype(np.uint32)
y = gather_nd_impl(x1, x2, batch_dims=2)
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "gather_nd_fp8x23_3d_batch_dims2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(2))",
name= name)
default()
batch_dims1()
batch_dims2()
gather_nd_3D()
@staticmethod
def gather_nd_i8():
def gather_nd_3D():
def default():
x1 = np.arange(0,9).reshape(3,3).astype(np.int8)
x2 = np.random.randint(low = 0,high=2, size=(3,2)).astype(np.int8)
y = gather_nd_impl(x1, x2, batch_dims=0)
x1 = Tensor(Dtype.I8, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "gather_nd_i8_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(0))",
name= name)
def batch_dims1():
x1 = np.arange(0,9).reshape(3,3).astype(np.int8)
x2 = np.random.randint(low = 0,high=2, size=(3,1)).astype(np.int8)
y = gather_nd_impl(x1, x2, batch_dims=1)
x1 = Tensor(Dtype.I8, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "gather_nd_i8_3d_batch_dims1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(1))",
name= name)
default()
batch_dims1()
gather_nd_3D()
@staticmethod
def gather_nd_i32():
def gather_nd_3D():
def default():
x1 = np.arange(0,24).reshape(4,2,3).astype(np.int32)
x2 = np.random.randint(low = 0,high=2, size=(3,2)).astype(np.int32)
y = gather_nd_impl(x1, x2, batch_dims=0)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "gather_nd_i32_3d_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(0))",
name= name)
def batch_dims1():
x1 = np.arange(0,108).reshape(4,3,3,3).astype(np.int32)
x2 = np.random.randint(low = 0,high=3, size=(4,2,3)).astype(np.uint32)
y = gather_nd_impl(x1, x2, batch_dims=1)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "gather_nd_i32_3d_batch_dims1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(1))",
name= name)
def batch_dims2():
x1 = np.arange(0,54).reshape(3,3,3,2).astype(np.int64)
x2 = np.random.randint(low = 0,high=2, size=(3,3,2)).astype(np.uint32)
y = gather_nd_impl(x1, x2, batch_dims=2)
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "gather_nd_i32_3d_batch_dims2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(2))",
name= name)
default()
batch_dims1()
batch_dims2()
gather_nd_3D()
@staticmethod
def gather_nd_u32():
def gather_nd_3D():
def default():
x1 = np.arange(0,108).reshape(3,3,4,3).astype(np.int32)
x2 = np.random.randint(low = 0,high=2, size=(3,3,2)).astype(np.uint32)
y = gather_nd_impl(x1, x2, batch_dims=0)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "gather_nd_u32_default"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(0))",
name= name)
def batch_dims1():
x1 = np.arange(0,108).reshape(3,3,4,3).astype(np.int32)
x2 = np.random.randint(low = 0,high=2, size=(3,3,2)).astype(np.uint32)
y = gather_nd_impl(x1, x2, batch_dims=1)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "gather_nd_u32_batch_dims1"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(1))",
name= name)
def batch_dims2():
x1 = np.arange(0,108).reshape(3,3,4,3).astype(np.int32)
x2 = np.random.randint(low = 0,high=2, size=(3,3,2)).astype(np.uint32)
y = gather_nd_impl(x1, x2, batch_dims=2)
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "gather_nd_u32_batch_dims2"
make_test(
inputs = [x1, x2], output = y, func_sig = "input_0.gather_nd(indices:input_1, batch_dims:Option::Some(2))",
name= name)
default()
batch_dims1()
batch_dims2()
gather_nd_3D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/gemm.py
|
from typing import Optional
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def gemm_reference_implementation(
A: np.ndarray,
B: np.ndarray,
C: Optional[np.ndarray] = None,
alpha: float = 1.0,
beta: float = 1.0,
transA: int = 0,
transB: int = 0,
) -> np.ndarray:
A = A if transA == 0 else A.T
B = B if transB == 0 else B.T
C = C if C is not None else np.array(0)
Y = alpha * np.dot(A, B) + beta * C
return Y
class Gemm(RunAll):
@staticmethod
def gemm_default_zero_bias():
a = np.random.ranf([3, 5]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gemm_default_no_bias"
make_test(
[a, b], y, "NNTrait::gemm(input_0, input_1, Option::None(()), Option::None(()), Option::None(()), false, false)", name, Trait.NN)
@staticmethod
def gemm_default_vector_bias():
a = np.random.ranf([2, 7]).astype(np.float32)
b = np.random.ranf([7, 4]).astype(np.float32)
c = np.random.ranf([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
c = Tensor(Dtype.FP16x16, c.shape, to_fp(
c.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gemm_default_vector_bias"
make_test(
[a, b, c], y, "NNTrait::gemm(input_0, input_1, Option::Some(input_2), Option::None(()), Option::None(()), false, false)", name, Trait.NN)
@staticmethod
def gemm_default_matrix_bias():
a = np.random.ranf([3, 6]).astype(np.float32)
b = np.random.ranf([6, 4]).astype(np.float32)
c = np.random.ranf([3, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
c = Tensor(Dtype.FP16x16, c.shape, to_fp(
c.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gemm_default_matrix_bias"
make_test(
[a, b, c], y, "NNTrait::gemm(input_0, input_1, Option::Some(input_2), Option::None(()), Option::None(()), false, false)", name, Trait.NN)
@staticmethod
def gemm_transposeA():
a = np.random.ranf([6, 3]).astype(np.float32)
b = np.random.ranf([6, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, transA=1)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gemm_transposeA"
make_test(
[a, b], y, "NNTrait::gemm(input_0, input_1, Option::None(()), Option::None(()), Option::None(()), true, false)", name, Trait.NN)
@staticmethod
def gemm_transposeB():
a = np.random.ranf([3, 6]).astype(np.float32)
b = np.random.ranf([4, 6]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, transB=1)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gemm_transposeB"
make_test(
[a, b], y, "NNTrait::gemm(input_0, input_1, Option::None(()), Option::None(()), Option::None(()), false, true)", name, Trait.NN)
@staticmethod
def gemm_alpha():
a = np.random.ranf([3, 5]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, alpha=0.5)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gemm_alpha"
make_test(
[a, b], y, "NNTrait::gemm(input_0, input_1, Option::None(()), Option::Some(FixedTrait::new(32768, false)), Option::None(()), false, false)", name, Trait.NN)
@staticmethod
def gemm_beta():
a = np.random.ranf([2, 7]).astype(np.float32)
b = np.random.ranf([7, 4]).astype(np.float32)
c = np.random.ranf([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, beta=0.5)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
c = Tensor(Dtype.FP16x16, c.shape, to_fp(
c.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gemm_beta"
make_test(
[a, b, c], y, "NNTrait::gemm(input_0, input_1, Option::Some(input_2), Option::None(()), Option::Some(FixedTrait::new(32768, false)), false, false)", name, Trait.NN)
@staticmethod
def gemm_all_attributes():
a = np.random.ranf([4, 3]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.random.ranf([1, 5]).astype(np.float32)
y = gemm_reference_implementation(
a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35
)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
c = Tensor(Dtype.FP16x16, c.shape, to_fp(
c.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "gemm_all_attributes"
make_test(
[a, b, c], y, "NNTrait::gemm(input_0, input_1, Option::Some(input_2), Option::Some(FixedTrait::new(16384, false)), Option::Some(FixedTrait::new(22938, false)), true, true)", name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/greater.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Greater(RunAll):
@staticmethod
def greater_u32():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.greater(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_u32"
make_test([x, y], z, "input_0.greater(@input_1)", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.uint32)
y = np.random.randint(0, 6, (1, 2)).astype(np.uint32)
z = np.greater(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_u32_broadcast"
make_test([x, y], z, "input_0.greater(@input_1)", name)
default()
broadcast()
@staticmethod
def greater_i32():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
z = np.greater(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_i32"
make_test([x, y], z, "input_0.greater(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int32)
y = np.random.randint(-3, 3, (1, 2)).astype(np.int32)
z = np.greater(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_i32_broadcast"
make_test([x, y], z, "input_0.greater(@input_1)", name)
default()
broadcast()
@staticmethod
def greater_i8():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
z = np.greater(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_i8"
make_test([x, y], z, "input_0.greater(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int8)
y = np.random.randint(-3, 3, (1, 2)).astype(np.int8)
z = np.greater(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_i8_broadcast"
make_test([x, y], z, "input_0.greater(@input_1)", name)
default()
broadcast()
@staticmethod
def greater_fp8x23():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.greater(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_fp8x23"
make_test([x, y], z, "input_0.greater(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.greater(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_fp8x23_broadcast"
make_test([x, y], z, "input_0.greater(@input_1)", name)
default()
broadcast()
@staticmethod
def greater_fp16x16():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.greater(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_fp16x16"
make_test([x, y], z, "input_0.greater(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.greater(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_fp16x16_broadcast"
make_test([x, y], z, "input_0.greater(@input_1)", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/greater_equal.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Greater_equal(RunAll):
@staticmethod
def greater_equal_u32():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.greater_equal(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_equal_u32"
make_test([x, y], z, "input_0.greater_equal(@input_1)", name)
def broadcast():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (1, 3, 1)).astype(np.uint32)
z = np.greater_equal(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_equal_u32_broadcast"
make_test([x, y], z, "input_0.greater_equal(@input_1)", name)
default()
broadcast()
@staticmethod
def greater_equal_i32():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
z = np.greater_equal(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_equal_i32"
make_test([x, y], z, "input_0.greater_equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.int32)
z = np.greater_equal(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_equal_i32_broadcast"
make_test([x, y], z, "input_0.greater_equal(@input_1)", name)
default()
broadcast()
@staticmethod
def greater_equal_i8():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
z = np.greater_equal(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_equal_i8"
make_test([x, y], z, "input_0.greater_equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.int8)
z = np.greater_equal(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_equal_i8_broadcast"
make_test([x, y], z, "input_0.greater_equal(@input_1)", name)
default()
broadcast()
@staticmethod
def greater_equal_fp8x23():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.greater_equal(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_equal_fp8x23"
make_test([x, y], z, "input_0.greater_equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.float64)
z = np.greater_equal(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_equal_fp8x23_broadcast"
make_test([x, y], z, "input_0.greater_equal(@input_1)", name)
default()
broadcast()
@staticmethod
def greater_equal_fp16x16():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.greater_equal(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_equal_fp16x16"
make_test([x, y], z, "input_0.greater_equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.float64)
z = np.greater_equal(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "greater_equal_fp16x16_broadcast"
make_test([x, y], z, "input_0.greater_equal(@input_1)", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/grid_sample.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
from .resize import _get_all_coords
import numbers
from typing import List
import numpy as np
#from onnx.reference.ops.op_resize import _get_all_coords
def grid_sample(X, grid, mode='linear', padding_mode='zeros', align_corners=0):
x_dims = X.shape
grid_dims = grid.shape
N = x_dims[0]
C = x_dims[1]
y_dims = (N, C, *grid_dims[1:-1])
if np.prod(y_dims) == 0:
return np.array([], dtype=X.dtype)
Y = np.empty(y_dims, dtype=X.dtype)
for n in range(N):
grid_data = grid[n]
for c in range(C):
X_data = X[n, c]
num_dims = len(x_dims[2:])
dims = x_dims[2:]
border = _prepare_border(dims, align_corners=align_corners)
for ox in _get_all_coords(Y[n, c]):
nx = grid_data[tuple(ox)]
nx = nx[::-1]
x = _gs_denormalize_coordinates(
n=nx, dims=dims, align_corners=align_corners
)
if mode == "nearest":
x = np.rint(x)
for i, v in enumerate(x):
x_min = border[i]
x_max = border[i + num_dims]
if v < x_min or v > x_max:
if padding_mode == "border":
x[i] = _clamp(v, 0, dims[i] - 1)
elif padding_mode == "reflection":
x[i] = _gs_reflect(v, x_min, x_max)
if mode == "nearest":
x = x.astype(np.int32)
Y[n][c][tuple(ox)] = _pixel_at_ndarray(
ndarray=X_data,
x=x,
border=border,
padding_mode=padding_mode,
)
elif mode == "linear":
Y[n][c][tuple(ox)] = _gs_linear_interpolation_nd_with_x(
data=X_data, x=x, border=border, padding_mode=padding_mode
)
elif mode == "cubic":
Y[n][c][tuple(ox)] = _gs_cubic_interpolation_nd_with_x(
data=X_data, x=x, border=border, padding_mode=padding_mode
)
else:
raise RuntimeError(
"GridSample interpolation only supports nearest, linear, and cubic modes."
)
return (Y.astype(X.dtype),)
def _gs_denormalize(n, length: int, align_corners: bool):
if align_corners:
x = (n + 1) / 2.0 * (length - 1)
else:
x = ((n + 1) * length - 1) / 2.0
return x
def _gs_denormalize_coordinates(n, dims, align_corners: bool):
x = np.zeros(len(n), dtype=np.float32)
for i, (v, dim) in enumerate(zip(n, dims)):
x[i] = _gs_denormalize(n=v, length=dim, align_corners=align_corners)
return x
def _gs_reflect(x, x_min, x_max): # type: ignore
"""Reflect by the near border till within the borders
Use float for borders to avoid potential issues with integer T
"""
fx = x
rng = x_max - x_min
if fx < x_min:
dx = x_min - fx
n = int(dx / rng)
r = dx - n * rng
if n % 2 == 0:
fx = x_min + r
else:
fx = x_max - r
elif fx > x_max:
dx = fx - x_max
n = int(dx / rng)
r = dx - n * rng
if n % 2 == 0:
fx = x_max - r
else:
fx = x_min + r
return fx
def _gs_get_cubic_coeffs(x, coeffs): # type: ignore
"""Calculate cubic convolution interpolation coefficients
ROBERT G. KEYS https://ieeexplore.ieee.org/document/1163711
Use float to avoid potential issues with integer.
"""
cubic_alpha = -0.75
x = abs(x)
coeffs[0] = (
(cubic_alpha * (x + 1) - 5 * cubic_alpha) * (x + 1) + 8 * cubic_alpha
) * (x + 1) - 4 * cubic_alpha
coeffs[1] = ((cubic_alpha + 2) * x - (cubic_alpha + 3)) * x * x + 1
coeffs[2] = ((cubic_alpha + 2) * (1 - x) - (cubic_alpha + 3)) * (1 - x) * (
1 - x
) + 1
coeffs[3] = (
(cubic_alpha * (2 - x) - 5 * cubic_alpha) * (2 - x) + 8 * cubic_alpha
) * (2 - x) - 4 * cubic_alpha
def _gs_get_linear_coeffs(x, coeffs):
x = abs(x)
coeffs[0] = 1 - x
coeffs[1] = x
def _gs_bicubic_interpolate(p, x, y): # type: ignore
v = np.empty((4,), dtype=p.dtype)
coeffs = np.empty((4,), dtype=p.dtype)
_gs_get_cubic_coeffs(x, coeffs)
for i in range(4):
v[i] = coeffs @ p[i, :]
_gs_get_cubic_coeffs(y, coeffs)
return coeffs @ v
def _gs_cubic_interpolation_1d_with_x(data, x, border, padding_mode):
v = np.empty((4,), dtype=data.dtype)
coeffs = np.empty((4,), dtype=data.dtype)
x_0 = int(np.floor(x))
x_1 = x_0 + 1
x_2 = x_0 + 2
x_minus_1 = x_0 - 1
_gs_get_cubic_coeffs(x - x_0, coeffs)
v[0] = _pixel_at_array(
array=data, i=x_minus_1, border=border, padding_mode=padding_mode
)
v[1] = _pixel_at_array(
array=data, i=x_0, border=border, padding_mode=padding_mode
)
v[2] = _pixel_at_array(
array=data, i=x_1, border=border, padding_mode=padding_mode
)
v[3] = _pixel_at_array(
array=data, i=x_2, border=border, padding_mode=padding_mode
)
return coeffs @ v
def _gs_linear_interpolation_1d_with_x(data, x, border, padding_mode):
v = np.empty((2,), dtype=data.dtype)
coeffs = np.empty((2,), dtype=data.dtype)
x_0 = int(np.floor(x))
x_1 = x_0 + 1
_gs_get_linear_coeffs(x - x_0, coeffs)
v[0] = _pixel_at_array(
array=data, i=x_0, border=border, padding_mode=padding_mode
)
v[1] = _pixel_at_array(
array=data, i=x_1, border=border, padding_mode=padding_mode
)
return coeffs @ v
def _gs_linear_interpolation_nd_with_x(data, x, border, padding_mode):
num_dims = data.ndim
assert num_dims == len(x) == int(len(border) / 2)
if num_dims == 1:
return _gs_linear_interpolation_1d_with_x(
data=data, x=x[0], border=border, padding_mode=padding_mode
)
res1d = []
for i in range(data.shape[0]):
r = _gs_linear_interpolation_nd_with_x(
data=data[i],
x=x[1:],
border=list(border[1:num_dims])
+ list(border[1 + num_dims : 2 * num_dims]),
padding_mode=padding_mode,
)
res1d.append(r)
res1d = np.array(res1d)
return _gs_linear_interpolation_1d_with_x(
data=res1d,
x=x[0],
border=[border[0], border[num_dims]],
padding_mode=padding_mode,
)
def _gs_cubic_interpolation_nd_with_x(data, x, border, padding_mode):
num_dims = data.ndim
assert num_dims == len(x) == int(len(border) / 2)
if num_dims == 1:
return _gs_cubic_interpolation_1d_with_x(
data=data, x=x[0], border=border, padding_mode=padding_mode
)
res1d = []
for i in range(data.shape[0]):
r = _gs_cubic_interpolation_nd_with_x(
data=data[i],
x=x[1:],
border=list(border[1:num_dims])
+ list(border[1 + num_dims : 2 * num_dims]),
padding_mode=padding_mode,
)
res1d.append(r)
res1d = np.array(res1d)
return _gs_cubic_interpolation_1d_with_x(
data=res1d,
x=x[0],
border=[border[0], border[num_dims]],
padding_mode=padding_mode,
)
def _clamp(val, lo, hi): # type: ignore
if val < lo:
return lo
if val > hi:
return hi
return val
def _pixel_at_ndarray(ndarray, x: List, border, padding_mode): # type: ignore
# boarder: [x_1_min, x_2_min, ..., x_1_max, x_2_max, ...]
num_dims = ndarray.ndim
assert num_dims == len(x) == int(len(border) / 2)
if num_dims == 1:
return _pixel_at_array(
array=ndarray, i=x[0], border=border, padding_mode=padding_mode
)
i = x[0]
d = ndarray.shape[0]
if padding_mode == "zeros":
if i >= 0 and i < d:
ndarray = ndarray[i]
else:
# Trick
i = 0
ndarray = np.zeros_like(ndarray[i])
elif padding_mode == "border":
i = _clamp(i, 0, d - 1)
ndarray = ndarray[i]
else:
i = int(_gs_reflect(i, border[0], border[num_dims]))
ndarray = ndarray[i]
return _pixel_at_ndarray(
ndarray=ndarray,
x=x[1:],
border=list(border[1:num_dims]) + list(border[1 + num_dims : 2 * num_dims]),
padding_mode=padding_mode,
)
def _pixel_at_array(array, i: int, border, padding_mode): # type: ignore
assert array.ndim == 1
d = array.shape[0]
if padding_mode == "zeros":
if i >= 0 and i < d:
pixel = array[i]
else:
pixel = 0
elif padding_mode == "border":
i = _clamp(i, 0, d - 1)
pixel = array[i]
else:
i = int(_gs_reflect(i, border[0], border[1]))
pixel = array[i]
return pixel
def _prepare_border(dims, align_corners: bool):
# boarder: [x_1_min, x_2_min, ..., x_1_max, x_2_max, ...]
num_dims = len(dims)
borders = np.zeros(num_dims * 2)
for i in range(num_dims):
# min
borders[i] = -0.5
# max
borders[i + num_dims] = dims[i] - 0.5
if align_corners:
# min
borders[i] = 0.0
# max
borders[i + num_dims] = dims[i] - 1.0
return borders
class Grid_sample(RunAll):
@staticmethod
def export_gridsample() -> None:
x = np.array(
[
[
[
[0.0, 1.0, 2.0, 3.0],
[4.0, 5.0, 6.0, 7.0],
[8.0, 9.0, 10.0, 11.0],
[12.0, 13.0, 14.0, 15.0],
]
]
],
dtype=np.float32,
)
grid = np.array(
[
[
[
[-1.0000, -1.0000],
[-0.6000, -1.0000],
[-0.2000, -1.0000],
[0.2000, -1.0000],
[0.6000, -1.0000],
[1.0000, -1.0000],
],
[
[-1.0000, -0.6000],
[-0.6000, -0.6000],
[-0.2000, -0.6000],
[0.2000, -0.6000],
[0.6000, -0.6000],
[1.0000, -0.6000],
],
[
[-1.0000, -0.2000],
[-0.6000, -0.2000],
[-0.2000, -0.2000],
[0.2000, -0.2000],
[0.6000, -0.2000],
[1.0000, -0.2000],
],
[
[-1.0000, 0.2000],
[-0.6000, 0.2000],
[-0.2000, 0.2000],
[0.2000, 0.2000],
[0.6000, 0.2000],
[1.0000, 0.2000],
],
[
[-1.0000, 0.6000],
[-0.6000, 0.6000],
[-0.2000, 0.6000],
[0.2000, 0.6000],
[0.6000, 0.6000],
[1.0000, 0.6000],
],
[
[-1.0000, 1.0000],
[-0.6000, 1.0000],
[-0.2000, 1.0000],
[0.2000, 1.0000],
[0.6000, 1.0000],
[1.0000, 1.0000],
],
]
],
dtype=np.float32,
)
y = grid_sample(x, grid, mode ="linear")
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
grid = Tensor(Dtype.FP16x16, grid.shape, to_fp(grid.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "grid_sample"
func_sig = "NNTrait::grid_sample("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, grid], y, func_sig, name, Trait.NN)
@staticmethod
def export_gridsample_paddingmode_zeros() -> None:
x = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
grid = np.array(
[
[
[
[-10.0000, -10.0000],
[-5.0000, -5.0000],
[-0.2000, -0.2000],
[10.0000, 10.0000],
],
[
[10.0000, 10.0000],
[-0.2000, -0.2000],
[5.0000, 5.0000],
[10.0000, 10.0000],
],
]
],
dtype=np.float32,
)
y = grid_sample(x, grid, mode ="linear")
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
grid = Tensor(Dtype.FP16x16, grid.shape, to_fp(grid.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "grid_sample_padding_zeros"
func_sig = "NNTrait::grid_sample("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, grid], y, func_sig, name, Trait.NN)
@staticmethod
def export_gridsample_paddingmode_border() -> None:
x = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
grid = np.array(
[
[
[
[-10.0000, -10.0000],
[-5.0000, -5.0000],
[-0.2000, -0.2000],
[10.0000, 10.0000],
],
[
[10.0000, 10.0000],
[-0.2000, -0.2000],
[5.0000, 5.0000],
[10.0000, 10.0000],
],
]
],
dtype=np.float32,
)
y = grid_sample(x, grid, mode ="linear", padding_mode="border")
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
grid = Tensor(Dtype.FP16x16, grid.shape, to_fp(grid.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "grid_sample_padding_border"
func_sig = "NNTrait::grid_sample("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(PADDING_MODE::BORDER))"
make_test(
[x, grid], y, func_sig, name, Trait.NN)
@staticmethod
def export_gridsample_paddingmode_reflection() -> None:
x = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
grid = np.array(
[
[
[
[-10.0000, -10.0000],
[-5.0000, -5.0000],
[-0.2000, -0.2000],
[10.0000, 10.0000],
],
[
[10.0000, 10.0000],
[-0.2000, -0.2000],
[5.0000, 5.0000],
[10.0000, 10.0000],
],
]
],
dtype=np.float32,
)
y = grid_sample(x, grid, mode ="linear", padding_mode="reflection")
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
grid = Tensor(Dtype.FP16x16, grid.shape, to_fp(grid.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "grid_sample_padding_reflection"
func_sig = "NNTrait::grid_sample("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(PADDING_MODE::REFLECTION))"
make_test(
[x, grid], y, func_sig, name, Trait.NN)
@staticmethod
def export_gridsample_mode_aligncorners() -> None:
x = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
grid = np.array(
[
[
[
[-1.0000, -1.0000],
[-0.5000, -0.5000],
[-0.2000, -0.2000],
[0.0000, 0.0000],
],
[
[0.0000, 0.0000],
[-0.2000, -0.2000],
[0.5000, 0.5000],
[1.0000, 1.0000],
],
]
],
dtype=np.float32,
)
y = grid_sample(x, grid, mode ="linear", align_corners=1)
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
grid = Tensor(Dtype.FP16x16, grid.shape, to_fp(grid.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "grid_sample_aligncorners"
func_sig = "NNTrait::grid_sample("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::Some(1),"
func_sig += "Option::None,"
func_sig += "Option::None)"
make_test(
[x, grid], y, func_sig, name, Trait.NN)
@staticmethod
def export_gridsample_nearest() -> None:
x = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
grid = np.array(
[
[
[
[-1.0000, -1.0000],
[-0.5000, -0.5000],
[-0.2000, -0.2000],
[0.0000, 0.0000],
],
[
[0.0000, 0.0000],
[-0.2000, -0.2000],
[0.5000, 0.5000],
[1.0000, 1.0000],
],
]
],
dtype=np.float32,
)
y = grid_sample(x, grid, mode ="nearest", align_corners=0)
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
grid = Tensor(Dtype.FP16x16, grid.shape, to_fp(grid.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "grid_sample_nearest"
func_sig = "NNTrait::grid_sample("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::Some(0),"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None)"
make_test(
[x, grid], y, func_sig, name, Trait.NN)
@staticmethod
def export_gridsample_nearest_align_corner() -> None:
x = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
grid = np.array(
[
[
[
[-1.0000, -1.0000],
[-0.5000, -0.5000],
[-0.2000, -0.2000],
[0.0000, 0.0000],
],
[
[0.0000, 0.0000],
[-0.2000, -0.2000],
[0.5000, 0.5000],
[1.0000, 1.0000],
],
]
],
dtype=np.float32,
)
y = grid_sample(x, grid, mode ="nearest", align_corners=1)
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
grid = Tensor(Dtype.FP16x16, grid.shape, to_fp(grid.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "grid_sample_nearest_aligncorner"
func_sig = "NNTrait::grid_sample("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::Some(1),"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None)"
make_test(
[x, grid], y, func_sig, name, Trait.NN)
@staticmethod
def export_gridsample_cubic() -> None:
x = np.array(
[[[[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]]]],
dtype=np.float32,
)
grid = np.array(
[
[
[
[-1.0000, -1.0000],
[-0.5000, -0.5000],
[-0.2000, -0.2000],
[0.0000, 0.0000],
],
[
[0.0000, 0.0000],
[-0.2000, -0.2000],
[0.5000, 0.5000],
[1.0000, 1.0000],
],
]
],
dtype=np.float32,
)
y = grid_sample(x, grid, mode ="cubic", align_corners=0)
y = np.array(y[0])
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
grid = Tensor(Dtype.FP16x16, grid.shape, to_fp(grid.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "grid_sample_cubic"
func_sig = "NNTrait::grid_sample("
func_sig += "@input_0,"
func_sig += "@input_1,"
func_sig += "Option::Some(0),"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None)"
make_test(
[x, grid], y, func_sig, name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/hamming_window.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait, get_data_statement
def hamming_window(size, output_datatype=None, periodic=None) -> np.ndarray: # type: ignore
if periodic == 1:
N_1 = size
else:
N_1 = size - 1
ni = np.arange(size, dtype=output_datatype)
alpha = 25.0 / 46.0
beta = 1 - alpha
res = alpha - np.cos(ni * np.float64(np.pi).astype(output_datatype) * 2 / N_1).astype(output_datatype) * beta
return res.astype(output_datatype)
class Hamming_window(RunAll):
@staticmethod
# We test here with fp8x23 implementation.
def fp8x23():
args = [4]
# x = np.float64(4)
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP8x23), Dtype.FP8x23)
y = hamming_window(*args, np.float64)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
# Define the name of the generated folder.
name = "hamming_window_fp8x23"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::hamming_window({','.join(args_str)}, Option::Some(0))", # The code signature.
name # The name of the generated folder.
)
@staticmethod
# We test here with fp16x16 implementation.
def fp16x16():
args = [10]
# x = np.float64(4)
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP16x16), Dtype.FP16x16)
y = hamming_window(*args, np.float16)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
# Define the name of the generated folder.
name = "hamming_window_fp16x16"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::hamming_window({','.join(args_str)}, Option::Some(0))", # The code signature.
name # The name of the generated folder.
)
# @staticmethod
# # We test here with i8 implementation.
# def i8():
# print(get_data_statement(np.array([np.pi]).flatten(), Dtype.I8))
# args = [5]
# # x = np.float64(4)
# args_str = get_data_statement(np.array(args).flatten(), Dtype.I8)
# y = hamming_window(*args, np.int8)
# print(y)
# # Convert the floats values in `y` to fixed points with `to_fp` method:
# y = Tensor(Dtype.I8, y.shape, y.flatten())
# # Define the name of the generated folder.
# name = "hamming_window_i8"
# # Invoke `make_test` method to generate corresponding Cairo tests:
# make_test(
# [], # List of input tensors.
# y, # The expected output result.
# f"TensorTrait::hamming_window({','.join(args_str)}, Option::Some(1))", # The code signature.
# name # The name of the generated folder.
# )
# @staticmethod
# # We test here with i32 implementation.
# def i32():
# print(get_data_statement(np.array([np.pi]).flatten(), Dtype.I32))
# args = [4]
# # x = np.float64(4)
# args_str = get_data_statement(np.array(args).flatten(), Dtype.I32)
# y = hamming_window(*args, np.int32)
# print(y)
# # Convert the floats values in `y` to fixed points with `to_fp` method:
# y = Tensor(Dtype.I32, y.shape, y.flatten())
# # Define the name of the generated folder.
# name = "hamming_window_i32"
# # Invoke `make_test` method to generate corresponding Cairo tests:
# make_test(
# [], # List of input tensors.
# y, # The expected output result.
# f"TensorTrait::hamming_window({','.join(args_str)}, Option::Some(0))", # The code signature.
# name # The name of the generated folder.
# )
# @staticmethod
# # We test here with u32 implementation.
# def u32():
# print(get_data_statement(np.array([np.pi]).flatten(), Dtype.U32))
# args = [4]
# # x = np.float64(4)
# args_str = get_data_statement(np.array(args).flatten(), Dtype.U32)
# y = hamming_window(*args, np.uint32)
# print(y)
# # Convert the floats values in `y` to fixed points with `to_fp` method:
# y = Tensor(Dtype.U32, y.shape, y.flatten())
# # Define the name of the generated folder.
# name = "hamming_window_u32"
# # Invoke `make_test` method to generate corresponding Cairo tests:
# make_test(
# [], # List of input tensors.
# y, # The expected output result.
# f"TensorTrait::hamming_window({','.join(args_str)}, Option::Some(0))", # The code signature.
# name # The name of the generated folder.
# )
|
https://github.com/gizatechxyz/orion
|
nodegen/node/hann_window.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait, get_data_statement
def hann_window(size, output_datatype=None, periodic=None) -> np.ndarray: # type: ignore
if periodic == 1:
N_1 = size
else:
N_1 = size - 1
ni = np.arange(size, dtype=output_datatype)
res = np.sin((ni * np.float64(np.pi).astype(output_datatype) / N_1).astype(output_datatype)) ** 2
return res.astype(output_datatype)
class Hann_window(RunAll):
@staticmethod
# We test here with fp8x23 implementation.
def fp8x23():
print(get_data_statement(to_fp(np.array([np.pi]).flatten(), FixedImpl.FP8x23), Dtype.FP8x23))
args = [4]
# x = np.float64(4)
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP8x23), Dtype.FP8x23)
y = hann_window(*args, np.float64)
print(y)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
# Define the name of the generated folder.
name = "hann_window_fp8x23"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::hann_window({','.join(args_str)}, Option::Some(0))", # The code signature.
name # The name of the generated folder.
)
@staticmethod
# We test here with fp16x16 implementation.
def fp16x16():
print(get_data_statement(to_fp(np.array([np.pi]).flatten(), FixedImpl.FP16x16), Dtype.FP16x16))
args = [10]
# x = np.float64(4)
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP16x16), Dtype.FP16x16)
y = hann_window(*args, np.float16)
print(y)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
# Define the name of the generated folder.
name = "hann_window_fp16x16"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::hann_window({','.join(args_str)}, Option::Some(0))", # The code signature.
name # The name of the generated folder.
)
# @staticmethod
# # We test here with i8 implementation.
# def i8():
# print(get_data_statement(np.array([np.pi]).flatten(), Dtype.I8))
# args = [5]
# # x = np.float64(4)
# args_str = get_data_statement(np.array(args).flatten(), Dtype.I8)
# y = hann_window(*args, np.int8)
# print(y)
# # Convert the floats values in `y` to fixed points with `to_fp` method:
# y = Tensor(Dtype.I8, y.shape, y.flatten())
# # Define the name of the generated folder.
# name = "hann_window_i8"
# # Invoke `make_test` method to generate corresponding Cairo tests:
# make_test(
# [], # List of input tensors.
# y, # The expected output result.
# f"TensorTrait::hann_window({','.join(args_str)}, Option::Some(1))", # The code signature.
# name # The name of the generated folder.
# )
# @staticmethod
# # We test here with i32 implementation.
# def i32():
# print(get_data_statement(np.array([np.pi]).flatten(), Dtype.I32))
# args = [4]
# # x = np.float64(4)
# args_str = get_data_statement(np.array(args).flatten(), Dtype.I32)
# y = hann_window(*args, np.int32)
# print(y)
# # Convert the floats values in `y` to fixed points with `to_fp` method:
# y = Tensor(Dtype.I32, y.shape, y.flatten())
# # Define the name of the generated folder.
# name = "hann_window_i32"
# # Invoke `make_test` method to generate corresponding Cairo tests:
# make_test(
# [], # List of input tensors.
# y, # The expected output result.
# f"TensorTrait::hann_window({','.join(args_str)}, Option::Some(0))", # The code signature.
# name # The name of the generated folder.
# )
# @staticmethod
# # We test here with u32 implementation.
# def u32():
# print(get_data_statement(np.array([np.pi]).flatten(), Dtype.U32))
# args = [4]
# # x = np.float64(4)
# args_str = get_data_statement(np.array(args).flatten(), Dtype.U32)
# y = hann_window(*args, np.uint32)
# print(y)
# # Convert the floats values in `y` to fixed points with `to_fp` method:
# y = Tensor(Dtype.U32, y.shape, y.flatten())
# # Define the name of the generated folder.
# name = "hann_window_u32"
# # Invoke `make_test` method to generate corresponding Cairo tests:
# make_test(
# [], # List of input tensors.
# y, # The expected output result.
# f"TensorTrait::hann_window({','.join(args_str)}, Option::Some(0))", # The code signature.
# name # The name of the generated folder.
# )
|
https://github.com/gizatechxyz/orion
|
nodegen/node/hard_sigmoid.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
class Hard_sigmoid(RunAll):
@staticmethod
def fp8x23():
alpha = 0.2
beta = 0.5
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float32)
y = np.maximum(0, np.minimum(1, alpha * x + beta))
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "hard_sigmoid_fp8x23"
make_test([x], y, "NNTrait::hard_sigmoid(@input_0, @FixedTrait::new(1677721, false), @FixedTrait::new(4194304, false))",
name, Trait.NN)
@staticmethod
def fp16x16():
alpha = 0.2
beta = 0.5
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float32)
y = np.maximum(0, np.minimum(1, alpha * x + beta))
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "hard_sigmoid_fp16x16"
make_test([x], y, "NNTrait::hard_sigmoid(@input_0, @FixedTrait::new(13107, false), @FixedTrait::new(32768, false))",
name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/identity.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Identity(RunAll):
@staticmethod
def identity_fP8x23():
def identity():
x = np.array([[1, 2], [3, 4]])
y = x
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "identity_fP8x23"
make_test(
[x], y, "input_0.identity()", name)
identity()
@staticmethod
def identity_fP16x16():
def identity():
x = np.array([[1, 2], [3, 4]])
y = x
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "identity_fP16x16"
make_test(
[x], y, "input_0.identity()", name)
identity()
@staticmethod
def identity_i8():
def identity():
x = np.array([[1, 2], [3, 4]])
y = x
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "identity_i8"
make_test(
[x], y, "input_0.identity()", name)
identity()
@staticmethod
def identity_i32():
def identity():
x = np.array([[1, 2], [3, 4]])
y = x
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "identity_i32"
make_test(
[x], y, "input_0.identity()", name)
identity()
@staticmethod
def identity_u32():
def identity():
x = np.array([[1, 2], [3, 4]])
y = x
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "identity_u32"
make_test(
[x], y, "input_0.identity()", name)
identity()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/is_inf.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
INF = 2**32 - 1
class Is_inf(RunAll):
@staticmethod
def is_inf_u32():
def default():
input_0 = np.array([1, 0, INF, 8, -INF, INF], dtype=np.uint32)
output = np.array([False, False, True, False, True, True], dtype=bool)
input_0 = Tensor(Dtype.U32, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_inf_u32"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::None, Option::None)", name)
default()
@staticmethod
def is_inf_i32():
def default():
input_0 = np.array([-1, 0, INF, 8, -INF, INF], dtype=np.int32)
output = np.array([False, False, True, False, True, True], dtype=bool)
input_0 = Tensor(Dtype.I32, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_inf_i32"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::None, Option::None)", name)
def positive():
input_0 = np.array([-1, 0, INF, 8, -INF, INF], dtype=np.int32)
output = np.array([False, False, True, False, False, True], dtype=bool)
input_0 = Tensor(Dtype.I32, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_pos_inf_i32"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::Some(0), Option::Some(1))", name)
def negative():
input_0 = np.array([-1, 0, INF, 8, -INF, INF], dtype=np.int32)
output = np.array([False, False, False, False, True, False], dtype=bool)
input_0 = Tensor(Dtype.I32, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_neg_inf_i32"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::Some(1), Option::Some(0))", name)
default()
positive()
negative()
@staticmethod
def is_inf_i8():
def default():
input_0 = np.array([-1, 0, INF, 8, -INF, INF], dtype=np.int8)
output = np.array([False, False, True, False, True, True], dtype=bool)
input_0 = Tensor(Dtype.I8, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_inf_i8"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::None, Option::None)", name)
def positive():
input_0 = np.array([-1, 0, INF, 8, -INF, INF], dtype=np.int32)
output = np.array([False, False, True, False, False, True], dtype=bool)
input_0 = Tensor(Dtype.I8, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_pos_inf_i8"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::Some(0), Option::Some(1))", name)
def negative():
input_0 = np.array([-1, 0, INF, 8, -INF, INF], dtype=np.int32)
output = np.array([False, False, False, False, True, False], dtype=bool)
input_0 = Tensor(Dtype.I8, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_neg_inf_i8"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::Some(1), Option::Some(0))", name)
default()
positive()
negative()
@staticmethod
def is_inf_fp8x23():
def default():
input_0 = np.array([-1.2, 0, INF, 2.8, -INF, INF], dtype=np.float64)
output = np.array([False, False, True, False, True, True], dtype=bool)
input_0 = Tensor(Dtype.FP8x23, input_0.shape, to_fp(
input_0.flatten(), FixedImpl.FP8x23))
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_inf_fp8x23"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::None, Option::None)", name)
def positive():
input_0 = np.array([-1.2, 0, INF, 2.8, -INF, INF], dtype=np.float64)
output = np.array([False, False, True, False, False, True], dtype=bool)
input_0 = Tensor(Dtype.FP8x23, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_pos_inf_fp8x23"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::Some(0), Option::Some(1))", name)
def negative():
input_0 = np.array([-1.2, 0, INF, 2.8, -INF, INF], dtype=np.float64)
output = np.array([False, False, False, False, True, False], dtype=bool)
input_0 = Tensor(Dtype.FP8x23, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_neg_inf_fp8x23"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::Some(1), Option::Some(0))", name)
default()
positive()
negative()
@staticmethod
def is_inf_fp16x16():
def default():
input_0 = np.array([-1.2, 0, INF, 2.8, -INF, INF], dtype=np.float64)
output = np.array([False, False, True, False, True, True], dtype=bool)
input_0 = Tensor(Dtype.FP16x16, input_0.shape, to_fp(
input_0.flatten(), FixedImpl.FP16x16))
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_inf_fp16x16"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::None, Option::None)", name)
def positive():
input_0 = np.array([-1.2, 0, INF, 2.8, -INF, INF], dtype=np.float64)
output = np.array([False, False, True, False, False, True], dtype=bool)
input_0 = Tensor(Dtype.FP16x16, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_pos_inf_fp16x16"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::Some(0), Option::Some(1))", name)
def negative():
input_0 = np.array([-1.2, 0, INF, 2.8, -INF, INF], dtype=np.float64)
output = np.array([False, False, False, False, True, False], dtype=bool)
input_0 = Tensor(Dtype.FP16x16, input_0.shape, input_0.flatten())
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_neg_inf_fp16x16"
make_test([input_0], output, "TensorTrait::is_inf(@input_0, Option::Some(1), Option::Some(0))", name)
default()
positive()
negative()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/is_nan.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
# NaN is represented with -0
NaN = -0
class Is_nan(RunAll):
@staticmethod
def is_nan_fp8x23():
def default():
input_0 = np.array([-1.2, 0, NaN, 2.8, NaN, NaN], dtype=np.float64)
output = np.array([False, False, True, False, True, True], dtype=bool)
input_0 = Tensor(Dtype.FP8x23, input_0.shape, to_fp(
input_0.flatten(), FixedImpl.FP8x23))
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_nan_fp8x23"
make_test([input_0], output, "TensorTrait::is_nan(@input_0)", name)
default()
@staticmethod
def is_nan_fp16x16():
def default():
input_0 = np.array([-1.2, 0, NaN, 2.8, NaN, NaN], dtype=np.float64)
output = np.array([False, False, True, False, True, True], dtype=bool)
input_0 = Tensor(Dtype.FP16x16, input_0.shape, to_fp(
input_0.flatten(), FixedImpl.FP16x16))
output = Tensor(Dtype.BOOL, output.shape, output.flatten())
name = "is_nan_fp16x16"
make_test([input_0], output, "TensorTrait::is_nan(@input_0)", name)
default()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/label_encoder.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
# Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
def labelEncoder( # type: ignore
x,
default_float=None,
default_int64=None,
default_string=None,
keys_floats=None,
keys_int64s=None,
keys_strings=None,
values_floats=None,
values_int64s=None,
values_strings=None,
):
keys = keys_floats if keys_floats is not None else (keys_int64s if np.any(keys_int64s) else keys_strings)
values = values_floats if values_floats is not None else (values_int64s if np.any(values_int64s) else values_strings)
classes = dict(zip(keys, values))
if id(keys) == id(keys_floats):
cast = float
elif id(keys) == id(keys_int64s):
cast = int # type: ignore
else:
cast = str # type: ignore
if id(values) == id(values_floats):
defval = default_float
dtype = np.float32
elif id(values) == id(values_int64s):
defval = default_int64
dtype = np.int64 # type: ignore
else:
defval = default_string
if not isinstance(defval, str):
defval = ""
dtype = np.str_ # type: ignore
shape = x.shape
if len(x.shape) > 1:
x = x.flatten()
res = []
for i in range(0, x.shape[0]):
v = classes.get(cast(x[i]), defval)
res.append(v)
return np.array(res, dtype=dtype).reshape(shape)
class Label_encoder(RunAll):
@staticmethod
def label_encoder_fp16x16():
def labelencoder():
def default():
x = np.array([1, 2, 3, 4, 5, 6, 1, 2, 3]).astype(np.int64)
keys = np.array([1, 2, 5, 6, ]).astype(np.int64)
values = np.array([11, 22, 55, 66]).astype(np.int64)
default = np.array(99).astype(np.int64)
y = labelEncoder(x=x, keys_int64s=keys, values_int64s=values, default_int64=default)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
default = Tensor(Dtype.FP16x16, default.shape, to_fp(default.flatten(), FixedImpl.FP16x16))
keys = Tensor(Dtype.FP16x16, keys.shape, to_fp(keys.flatten(), FixedImpl.FP16x16))
values = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "label_encoder_fp16x16_3d_default"
make_test(
inputs = [x, default, keys, values], output = y, func_sig = """input_0.label_encoder(default_list:Option::None, default_tensor: Option::Some(input_1),
keys:Option::None, keys_tensor: Option::Some(input_2),
values: Option::None, values_tensor: Option::Some(input_3))""",
name= name)
default()
labelencoder()
@staticmethod
def label_encoder_fp8x23():
def label_encoder():
def default():
x = np.array([1, 2, 3, 4, 5, 6, 1, 2, 3, 7, 8]).astype(np.int64)
keys = np.array([1, 2, 5, 6, 7]).astype(np.int64)
values = np.array([11, 22, 55, 66, 77]).astype(np.int64)
default = np.array(99).astype(np.int64)
y = labelEncoder(x=x, keys_int64s=keys, values_int64s=values, default_int64=default)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(x.flatten(), FixedImpl.FP8x23))
default = Tensor(Dtype.FP8x23, default.shape, to_fp(default.flatten(), FixedImpl.FP8x23))
keys = Tensor(Dtype.FP8x23, keys.shape, to_fp(keys.flatten(), FixedImpl.FP8x23))
values = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "label_encoder_fp8x23_default"
make_test(
inputs = [x, default, keys, values], output = y, func_sig = """input_0.label_encoder(default_list:Option::None, default_tensor: Option::Some(input_1),
keys:Option::None, keys_tensor: Option::Some(input_2),
values: Option::None, values_tensor: Option::Some(input_3))""",
name= name)
default()
label_encoder()
@staticmethod
def label_encoder_i8():
def label_encoder_3D():
def default():
x = np.array([1, 2, 3, 4, 5, 6, 1, 2, 3, 7, 8]).astype(np.int8)
keys = np.array([1, 2, 5, 6, 7]).astype(np.int8)
values = np.array([11, 22, 55, 66, 77]).astype(np.int8)
default = np.array(99).astype(np.int8)
y = labelEncoder(x=x, keys_int64s=keys, values_int64s=values, default_int64=default)
x = Tensor(Dtype.I8, x.shape, x.flatten())
default = Tensor(Dtype.I8, default.shape, default.flatten())
keys = Tensor(Dtype.I8, keys.shape, keys.flatten())
values = Tensor(Dtype.I8, values.shape, values.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "label_encoder_i8_default"
make_test(
inputs = [x, default, keys, values], output = y, func_sig = """input_0.label_encoder(default_list:Option::None, default_tensor: Option::Some(input_1),
keys:Option::None, keys_tensor: Option::Some(input_2),
values: Option::None, values_tensor: Option::Some(input_3))""",
name= name)
default()
label_encoder_3D()
@staticmethod
def label_encoder_i32():
def label_encoder_3D():
def default():
x = np.array([1, 2, 3, 4, 5, 6, 1, 2, 3, 7, 8]).astype(np.int32)
keys = np.array([1, 2, 5, 6, 7]).astype(np.int32)
values = np.array([11, 22, 55, 66, 77]).astype(np.int32)
default = np.array(99).astype(np.int32)
y = labelEncoder(x=x, keys_int64s=keys, values_int64s=values, default_int64=default)
x = Tensor(Dtype.I32, x.shape, x.flatten())
default = Tensor(Dtype.I32, default.shape, default.flatten())
keys = Tensor(Dtype.I32, keys.shape, keys.flatten())
values = Tensor(Dtype.I32, values.shape, values.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "label_encoder_i32_default"
make_test(
inputs = [x, default, keys, values], output = y, func_sig = """input_0.label_encoder(default_list:Option::None, default_tensor: Option::Some(input_1),
keys:Option::None, keys_tensor: Option::Some(input_2),
values: Option::None, values_tensor: Option::Some(input_3))""",
name= name)
default()
label_encoder_3D()
@staticmethod
def label_encoder_u32():
def label_encoder_3D():
def default():
x = np.array([1, 2, 3, 4, 5, 6, 1, 2, 3, 7, 8]).astype(np.uint32)
keys = np.array([1, 2, 5, 6, 7]).astype(np.uint32)
values = np.array([11, 22, 55, 66, 77]).astype(np.uint32)
default = np.array(99).astype(np.uint32)
y = labelEncoder(x=x, keys_int64s=keys, values_int64s=values, default_int64=default)
x = Tensor(Dtype.U32, x.shape, x.flatten())
default = Tensor(Dtype.U32, default.shape, default.flatten())
keys = Tensor(Dtype.U32, keys.shape, keys.flatten())
values = Tensor(Dtype.U32, values.shape, values.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "label_encoder_u32_default"
make_test(
inputs = [x, default, keys, values], output = y, func_sig = """input_0.label_encoder(default_list:Option::None, default_tensor: Option::Some(input_1),
keys:Option::None, keys_tensor: Option::Some(input_2),
values: Option::None, values_tensor: Option::Some(input_3))""",
name= name)
default()
label_encoder_3D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/layer_normalization.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def _layer_normalization(X, W, B, axis=-1, epsilon=1e-5):
X_shape = X.shape
X_rank = len(X_shape)
if axis < 0:
axis = axis + X_rank
unsqueezed_rank = X_rank - axis
reduction_shape = X_shape[0:axis] + (1,) * unsqueezed_rank
row_number = 1
col_number = 1
for i in range(X_rank):
if i < axis:
row_number *= X_shape[i]
else:
col_number *= X_shape[i]
x_mat = np.reshape(X, (row_number, col_number))
x_mean = np.sum(x_mat, axis=1, keepdims=True) / col_number
x_diff = x_mat - x_mean
x_squared_diff = x_diff * x_diff
variance = np.sum(x_squared_diff, axis=1, keepdims=True) / col_number
variance_eps = variance + epsilon
std_dev = np.sqrt(variance_eps)
inv_std_dev = np.reciprocal(std_dev)
y_mat = x_diff * inv_std_dev
Y = np.reshape(y_mat, X_shape) * W + B
X_mean = np.reshape(x_mean, reduction_shape)
X_inv_std_dev = np.reshape(inv_std_dev, reduction_shape)
return Y, X_mean, X_inv_std_dev
def calculate_normalized_shape(X_shape, axis):
X_rank = len(X_shape)
if axis < 0:
axis = axis + X_rank
return X_shape[axis:]
class Layer_normalization(RunAll):
@staticmethod
def export4d() -> None:
X = np.random.randn(2, 3, 4, 5).astype(np.float32)
def case(axis: int) -> None:
normalized_shape = calculate_normalized_shape(X.shape, axis)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis)
if axis < 0:
name = f"layer_normalization_4d_axis_negative_{-axis}"
func_sig = f"input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::Some({-axis}),Option::None,Option::None)"
else:
name = f"layer_normalization_4d_axis{axis}"
func_sig = f"input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::Some({axis}),Option::None,Option::None)"
x = Tensor(Dtype.FP8x23, X.shape, to_fp(X.flatten(), FixedImpl.FP8x23))
w = Tensor(Dtype.FP8x23, W.shape, to_fp(W.flatten(), FixedImpl.FP8x23))
b = Tensor(Dtype.FP8x23, B.shape, to_fp(B.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, Y.shape, to_fp(Y.flatten(), FixedImpl.FP8x23))
make_test([x,w,b], y, func_sig, name)
for i in range(len(X.shape)):
case(i)
case(i - len(X.shape))
@staticmethod
def export_default_axis() -> None:
X = np.random.randn(2, 3, 4, 5).astype(np.float32)
normalized_shape = calculate_normalized_shape(X.shape, -1)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
Y, mean, inv_std_dev = _layer_normalization(X, W, B)
x = Tensor(Dtype.FP16x16, X.shape, to_fp(X.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, W.shape, to_fp(W.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, B.shape, to_fp(B.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, Y.shape, to_fp(Y.flatten(), FixedImpl.FP16x16))
name = "layer_normalization_default_axis"
make_test([x,w,b], y, "input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::None,Option::None,Option::None)", name)
@staticmethod
def export3d_epsilon() -> None:
epsilon = 1e-1
X = np.random.randn(2, 3, 5).astype(np.float32)
def case(axis: int) -> None:
normalized_shape = calculate_normalized_shape(X.shape, axis)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis, epsilon)
if axis < 0:
name = f"layer_normalization_3d_axis_negative_{-axis}_epsilon"
func_sig = f"input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::Some({-axis}),Option::Some(FixedTrait::new(6554, false)),Option::None)"
else:
name = f"layer_normalization_3d_axis{axis}_epsilon"
func_sig = f"input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::Some({axis}),Option::Some(FixedTrait::new(6554, false)),Option::None)"
x = Tensor(Dtype.FP16x16, X.shape, to_fp(X.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, W.shape, to_fp(W.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, B.shape, to_fp(B.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, Y.shape, to_fp(Y.flatten(), FixedImpl.FP16x16))
make_test([x,w,b], y, func_sig, name)
for i in range(len(X.shape)):
case(i)
case(i - len(X.shape))
@staticmethod
def test_2d_example() -> None:
X = np.random.randn(3, 4).astype(np.float32)
def case(axis: int) -> None:
normalized_shape = calculate_normalized_shape(X.shape, axis)
W = np.random.randn(*normalized_shape).astype(np.float32)
B = np.random.randn(*normalized_shape).astype(np.float32)
Y, mean, inv_std_dev = _layer_normalization(X, W, B, axis=axis)
node = onnx.helper.make_node(
"LayerNormalization",
inputs=["X", "W", "B"],
outputs=["Y", "Mean", "InvStdDev"],
axis=axis,
)
x = Tensor(Dtype.FP16x16, X.shape, to_fp(X.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, W.shape, to_fp(W.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, B.shape, to_fp(B.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, Y.shape, to_fp(Y.flatten(), FixedImpl.FP16x16))
name = "layer_normalization_test"
make_test([x,w,b], y, "input_0.layer_normalization(@input_1,Option::Some(@input_2),Option::None,Option::None,Option::None)", name)
case(-1)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/leaky_relu.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
import tensorflow as tf
class Leaky_relu(RunAll):
@staticmethod
def leaky_relu_fp8x23():
x = np.random.uniform(-5, 7, (2, 2)).astype(np.float64)
layer = tf.keras.layers.LeakyReLU(alpha=0.1)
y = layer(x).numpy()
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "leaky_relu_fp8x23"
make_test([x], y, "NNTrait::leaky_relu(@input_0, @FixedTrait::new(838861, false))",
name, Trait.NN)
@staticmethod
def leaky_relu_fp16x16():
x = np.random.uniform(-5, 7, (2, 2)).astype(np.float64)
layer = tf.keras.layers.LeakyReLU(alpha=0.1)
y = layer(x).numpy()
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "leaky_relu_fp16x16"
make_test([x], y, "NNTrait::leaky_relu(@input_0, @FixedTrait::new(6554, false))",
name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/less.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Less(RunAll):
@staticmethod
def less_u32():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.less(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_u32"
make_test([x, y], z, "input_0.less(@input_1)", name)
def broadcast():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (1, 3, 1)).astype(np.uint32)
z = np.less(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_u32_broadcast"
make_test([x, y], z, "input_0.less(@input_1)", name)
default()
broadcast()
@staticmethod
def less_i32():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
z = np.less(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_i32"
make_test([x, y], z, "input_0.less(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.int32)
z = np.less(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_i32_broadcast"
make_test([x, y], z, "input_0.less(@input_1)", name)
default()
broadcast()
@staticmethod
def less_i8():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
z = np.less(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_i8"
make_test([x, y], z, "input_0.less(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.int8)
z = np.less(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_i8_broadcast"
make_test([x, y], z, "input_0.less(@input_1)", name)
default()
broadcast()
@staticmethod
def less_fp8x23():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.less(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_fp8x23"
make_test([x, y], z, "input_0.less(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.float64)
z = np.less(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_fp8x23_broadcast"
make_test([x, y], z, "input_0.less(@input_1)", name)
default()
broadcast()
@staticmethod
def less_fp16x16():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.less(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_fp16x16"
make_test([x, y], z, "input_0.less(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.float64)
z = np.less(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_fp16x16_broadcast"
make_test([x, y], z, "input_0.less(@input_1)", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/less_equal.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Less_equal(RunAll):
@staticmethod
def less_equal_u32():
def default():
x = np.random.randint(0, 6, (2, 2)).astype(np.uint32)
y = np.random.randint(0, 6, (2, 2)).astype(np.uint32)
z = np.less_equal(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_u32"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.uint32)
y = np.random.randint(0, 6, (1, 2)).astype(np.uint32)
z = np.less_equal(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_u32_broadcast"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
default()
broadcast()
@staticmethod
def less_equal_i32():
def default():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int32)
y = np.random.randint(-3, 3, (2, 2)).astype(np.int32)
z = np.less_equal(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_i32"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int32)
y = np.random.randint(-3, 3, (1, 2)).astype(np.int32)
z = np.less_equal(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_i32_broadcast"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
default()
broadcast()
@staticmethod
def less_equal_i8():
def default():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int8)
y = np.random.randint(-3, 3, (2, 2)).astype(np.int8)
z = np.less_equal(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_i8"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int8)
y = np.random.randint(-3, 3, (1, 2)).astype(np.int8)
z = np.less_equal(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_i8_broadcast"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
default()
broadcast()
@staticmethod
def less_equal_fp8x23():
def default():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
z = np.less_equal(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_fp8x23"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.less_equal(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_fp8x23_broadcast"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
default()
broadcast()
@staticmethod
def less_equal_fp16x16():
def default():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
z = np.less_equal(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_fp16x16"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.less_equal(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_fp16x16_broadcast"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/linear.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
from typing import Optional
def linear(
i: np.ndarray,
w: np.ndarray,
b: Optional[np.ndarray] = None,
) -> np.ndarray:
return np.dot(i, w.T) + b
class Linear(RunAll):
@staticmethod
def linear_i32():
i = np.random.randint(-5, 9, (3)).astype(np.int32)
w = np.random.randint(-5, 9, (2, 3)).astype(np.int32)
b = np.random.randint(-5, 9, (2)).astype(np.int32)
y = linear(i, w, b)
i = Tensor(Dtype.I32, i.shape, i.flatten())
w = Tensor(Dtype.I32, w.shape, w.flatten())
b = Tensor(Dtype.I32, b.shape, b.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "linear_i32"
make_test([i, w, b], y, "NNTrait::linear(input_0, input_1, input_2)",
name, Trait.NN)
@staticmethod
def linear_i8():
i = np.random.randint(-3, 3, (3)).astype(np.int8)
w = np.random.randint(-3, 3, (2, 3)).astype(np.int8)
b = np.random.randint(-3, 3, (2)).astype(np.int8)
y = linear(i, w, b)
i = Tensor(Dtype.I8, i.shape, i.flatten())
w = Tensor(Dtype.I8, w.shape, w.flatten())
b = Tensor(Dtype.I8, b.shape, b.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "linear_i8"
make_test([i, w, b], y, "NNTrait::linear(input_0, input_1, input_2)",
name, Trait.NN)
@staticmethod
def linear_u32():
i = np.random.randint(0, 6, (3)).astype(np.uint32)
w = np.random.randint(0, 6, (2, 3)).astype(np.uint32)
b = np.random.randint(0, 6, (2)).astype(np.uint32)
y = linear(i, w, b)
i = Tensor(Dtype.U32, i.shape, i.flatten())
w = Tensor(Dtype.U32, w.shape, w.flatten())
b = Tensor(Dtype.U32, b.shape, b.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "linear_u32"
make_test([i, w, b], y, "NNTrait::linear(input_0, input_1, input_2)",
name, Trait.NN)
@staticmethod
def linear_fp8x23():
i = np.random.uniform(-5, 7, (3)).astype(np.float64)
w = np.random.uniform(-5, 7, (2, 3)).astype(np.float64)
b = np.random.uniform(-5, 7, (2)).astype(np.float64)
y = linear(i, w, b)
i = Tensor(Dtype.FP8x23, i.shape, to_fp(
i.flatten(), FixedImpl.FP8x23))
w = Tensor(Dtype.FP8x23, w.shape, to_fp(
w.flatten(), FixedImpl.FP8x23))
b = Tensor(Dtype.FP8x23, b.shape, to_fp(
b.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "linear_fp8x23"
make_test([i, w, b], y, "NNTrait::linear(input_0, input_1, input_2)",
name, Trait.NN)
@staticmethod
def linear_fp16x16():
i = np.random.uniform(-5, 7, (3)).astype(np.float64)
w = np.random.uniform(-5, 7, (2, 3)).astype(np.float64)
b = np.random.uniform(-5, 7, (2)).astype(np.float64)
y = linear(i, w, b)
i = Tensor(Dtype.FP16x16, i.shape, to_fp(
i.flatten(), FixedImpl.FP16x16))
w = Tensor(Dtype.FP16x16, w.shape, to_fp(
w.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "linear_fp16x16"
make_test([i, w, b], y, "NNTrait::linear(input_0, input_1, input_2)",
name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/log.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Log(RunAll):
@staticmethod
def log_fp8x23():
x = np.random.uniform(1, 127, (2, 2)).astype(np.float64)
y = np.log(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "log_fp8x23"
make_test([x], y, "input_0.log()", name)
@staticmethod
def log_fp16x16():
x = np.random.uniform(1, 127, (2, 2)).astype(np.float64)
y = np.log(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "log_fp16x16"
make_test([x], y, "input_0.log()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/logsoftmax.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def logsoftmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
x_max = np.max(x, axis=axis, keepdims=True)
tmp = np.exp(x - x_max)
s = np.sum(tmp, axis=axis, keepdims=True)
return (x - x_max) - np.log(s)
class Logsoftmax(RunAll):
def logsoftmax_fp8x23():
def axis_0():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = logsoftmax(x, 0)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "logsoftmax_fp8x23_axis_0"
make_test([x], y, "NNTrait::logsoftmax(@input_0, 0)",
name, Trait.NN)
def axis_1():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = logsoftmax(x, 1)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "logsoftmax_fp8x23_axis_1"
make_test([x], y, "NNTrait::logsoftmax(@input_0, 1)",
name, Trait.NN)
axis_0()
axis_1()
def logsoftmax_fp16x16():
def axis_0():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = logsoftmax(x, 0)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "logsoftmax_fp16x16_axis_0"
make_test([x], y, "NNTrait::logsoftmax(@input_0, 0)",
name, Trait.NN)
def axis_1():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = logsoftmax(x, 1)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "logsoftmax_fp16x16_axis_1"
make_test([x], y, "NNTrait::logsoftmax(@input_0, 1)",
name, Trait.NN)
axis_0()
axis_1()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/matmul.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Matmul(RunAll):
@staticmethod
def matmul_u32():
def matmul_1D():
a = np.random.randint(0, 255, (3)).astype(np.uint32)
b = np.random.randint(0, 255, (3)).astype(np.uint32)
y = np.matmul(a, b).reshape((1))
a = Tensor(Dtype.U32, a.shape, a.flatten())
b = Tensor(Dtype.U32, b.shape, b.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "matmul_u32_1d"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_2x2():
a = np.random.randint(0, 255, (2, 2)).astype(np.uint32)
b = np.random.randint(0, 255, (2, 2)).astype(np.uint32)
y = np.matmul(a, b)
a = Tensor(Dtype.U32, a.shape, a.flatten())
b = Tensor(Dtype.U32, b.shape, b.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "matmul_u32_2x2"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_2x1():
a = np.random.randint(0, 255, (2, 1)).astype(np.uint32)
b = np.random.randint(0, 255, (1, 2)).astype(np.uint32)
y = np.matmul(a, b)
a = Tensor(Dtype.U32, a.shape, a.flatten())
b = Tensor(Dtype.U32, b.shape, b.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "matmul_u32_2x1"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_1x2():
a = np.random.randint(0, 255, (1, 2)).astype(np.uint32)
b = np.random.randint(0, 255, (2, 1)).astype(np.uint32)
y = np.matmul(a, b)
a = Tensor(Dtype.U32, a.shape, a.flatten())
b = Tensor(Dtype.U32, b.shape, b.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "matmul_u32_1x2"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
matmul_1D()
matmul_2x2()
matmul_2x1()
matmul_1x2()
@staticmethod
def matmul_i32():
def matmul_1D():
a = np.random.randint(-127, 127, (3)).astype(np.int32)
b = np.random.randint(-127, 127, (3)).astype(np.int32)
y = np.matmul(a, b).reshape((1))
a = Tensor(Dtype.I32, a.shape, a.flatten())
b = Tensor(Dtype.I32, b.shape, b.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "matmul_i32_1d"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_2x2():
a = np.random.randint(-127, 127, (2, 2)).astype(np.int32)
b = np.random.randint(-127, 127, (2, 2)).astype(np.int32)
y = np.matmul(a, b)
a = Tensor(Dtype.I32, a.shape, a.flatten())
b = Tensor(Dtype.I32, b.shape, b.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "matmul_i32_2x2"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_2x1():
a = np.random.randint(-127, 127, (2, 1)).astype(np.int32)
b = np.random.randint(-127, 127, (1, 2)).astype(np.int32)
y = np.matmul(a, b)
a = Tensor(Dtype.I32, a.shape, a.flatten())
b = Tensor(Dtype.I32, b.shape, b.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "matmul_i32_2x1"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_1x2():
a = np.random.randint(-127, 127, (1, 2)).astype(np.int32)
b = np.random.randint(-127, 127, (2, 1)).astype(np.int32)
y = np.matmul(a, b)
a = Tensor(Dtype.I32, a.shape, a.flatten())
b = Tensor(Dtype.I32, b.shape, b.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "matmul_i32_1x2"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
matmul_1D()
matmul_2x2()
matmul_2x1()
matmul_1x2()
@staticmethod
def matmul_i8():
def matmul_1D():
a = np.random.randint(-4, 5, (3)).astype(np.int8)
b = np.random.randint(-4, 5, (3)).astype(np.int8)
y = np.matmul(a, b).reshape((1))
a = Tensor(Dtype.I8, a.shape, a.flatten())
b = Tensor(Dtype.I8, b.shape, b.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "matmul_i8_1d"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_2x2():
a = np.random.randint(-4, 5, (2, 2)).astype(np.int8)
b = np.random.randint(-4, 5, (2, 2)).astype(np.int8)
y = np.matmul(a, b)
a = Tensor(Dtype.I8, a.shape, a.flatten())
b = Tensor(Dtype.I8, b.shape, b.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "matmul_i8_2x2"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_2x1():
a = np.random.randint(-4, 5, (2, 1)).astype(np.int8)
b = np.random.randint(-4, 5, (1, 2)).astype(np.int8)
y = np.matmul(a, b)
a = Tensor(Dtype.I8, a.shape, a.flatten())
b = Tensor(Dtype.I8, b.shape, b.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "matmul_i8_2x1"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_1x2():
a = np.random.randint(-4, 5, (1, 2)).astype(np.int8)
b = np.random.randint(-4, 5, (2, 1)).astype(np.int8)
y = np.matmul(a, b)
a = Tensor(Dtype.I8, a.shape, a.flatten())
b = Tensor(Dtype.I8, b.shape, b.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "matmul_i8_1x2"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
matmul_1D()
matmul_2x2()
matmul_2x1()
matmul_1x2()
@staticmethod
def matmul_fp8x23():
def matmul_1D():
a = np.random.randint(-3, 4, (3)).astype(np.int64)
b = np.random.randint(-3, 4, (3)).astype(np.int64)
y = np.matmul(a, b).reshape((1))
a = Tensor(Dtype.FP8x23, a.shape, to_fp(
a.flatten(), FixedImpl.FP8x23))
b = Tensor(Dtype.FP8x23, b.shape, to_fp(
b.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "matmul_fp8x23_1d"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_2x2():
a = np.random.randint(-3, 4, (2, 2)).astype(np.int64)
b = np.random.randint(-3, 4, (2, 2)).astype(np.int64)
y = np.matmul(a, b)
a = Tensor(Dtype.FP8x23, a.shape, to_fp(
a.flatten(), FixedImpl.FP8x23))
b = Tensor(Dtype.FP8x23, b.shape, to_fp(
b.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "matmul_fp8x23_2x2"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_2x1():
a = np.random.randint(-3, 4, (2, 1)).astype(np.int64)
b = np.random.randint(-3, 4, (1, 2)).astype(np.int64)
y = np.matmul(a, b)
a = Tensor(Dtype.FP8x23, a.shape, to_fp(
a.flatten(), FixedImpl.FP8x23))
b = Tensor(Dtype.FP8x23, b.shape, to_fp(
b.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "matmul_fp8x23_2x1"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_1x2():
a = np.random.randint(-3, 4, (1, 2)).astype(np.int64)
b = np.random.randint(-3, 4, (2, 1)).astype(np.int64)
y = np.matmul(a, b)
a = Tensor(Dtype.FP8x23, a.shape, to_fp(
a.flatten(), FixedImpl.FP8x23))
b = Tensor(Dtype.FP8x23, b.shape, to_fp(
b.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "matmul_fp8x23_1x2"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
matmul_1D()
matmul_2x2()
matmul_2x1()
matmul_1x2()
@staticmethod
def matmul_fp16x16():
def matmul_1D():
a = np.random.randint(-3, 4, (3)).astype(np.int64)
b = np.random.randint(-3, 4, (3)).astype(np.int64)
y = np.matmul(a, b).reshape((1))
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "matmul_fp16x16_1d"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_2x2():
a = np.random.randint(-3, 4, (2, 2)).astype(np.int64)
b = np.random.randint(-3, 4, (2, 2)).astype(np.int64)
y = np.matmul(a, b)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "matmul_fp16x16_2x2"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_2x1():
a = np.random.randint(-3, 4, (2, 1)).astype(np.int64)
b = np.random.randint(-3, 4, (1, 2)).astype(np.int64)
y = np.matmul(a, b)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "matmul_fp16x16_2x1"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
def matmul_1x2():
a = np.random.randint(-3, 4, (1, 2)).astype(np.int64)
b = np.random.randint(-3, 4, (2, 1)).astype(np.int64)
y = np.matmul(a, b)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "matmul_fp16x16_1x2"
make_test(
[a, b], y, "input_0.matmul(@input_1)", name)
matmul_1D()
matmul_2x2()
matmul_2x1()
matmul_1x2()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/max.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
class Max(RunAll):
@staticmethod
def max_u32_two_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.maximum(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "max_u32_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.uint32)
y = np.random.randint(0, 6, (1, 2)).astype(np.uint32)
z = np.maximum(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "max_u32_broadcast_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def max_i32_two_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32)
z = np.maximum(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "max_i32_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.int32)
y = np.random.randint(0, 6, (1, 2)).astype(np.int32)
z = np.maximum(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "max_i32_broadcast_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def max_i8_two_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
z = np.maximum(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name = "max_i8_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.int8)
y = np.random.randint(0, 6, (1, 2)).astype(np.int8)
z = np.maximum(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name = "max_i8_broadcast_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def max_fp8x23_two_tensors():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.maximum(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "max_fp8x23_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.maximum(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "max_fp8x23_broadcast_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def max_fp16x16_two_tensors():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.maximum(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "max_fp16x16_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.maximum(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "max_fp16x16_broadcast_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def max_u32_three_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
m = Tensor(Dtype.U32, m.shape, m.flatten())
name = "max_u32_three_tensors"
make_test([x, y, z], m, "TensorTrait::max(array![input_0, input_1, input_2].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.uint32)
y = np.random.randint(0, 6, (1, 2)).astype(np.uint32)
z = np.random.randint(0, 6, (1, 1)).astype(np.uint32)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
m = Tensor(Dtype.U32, m.shape, m.flatten())
name = "max_u32_broadcast_three_tensors"
make_test([x, y, z], m, "TensorTrait::max(array![input_0, input_1, input_2].span())", name)
default()
broadcast()
@staticmethod
def max_i32_three_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32)
z = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
m = Tensor(Dtype.I32, m.shape, m.flatten())
name = "max_i32_three_tensors"
make_test([x, y, z], m, "TensorTrait::max(array![input_0, input_1, input_2].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.int32)
y = np.random.randint(0, 6, (1, 2)).astype(np.int32)
z = np.random.randint(0, 6, (1, 1)).astype(np.int32)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
m = Tensor(Dtype.I32, m.shape, m.flatten())
name = "max_i32_broadcast_three_tensors"
make_test([x, y, z], m, "TensorTrait::max(array![input_0, input_1, input_2].span())", name)
default()
broadcast()
@staticmethod
def max_i8_three_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
z = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
m = Tensor(Dtype.I8, m.shape, m.flatten())
name = "max_i8_three_tensors"
make_test([x, y, z], m, "TensorTrait::max(array![input_0, input_1, input_2].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.int8)
y = np.random.randint(0, 6, (1, 2)).astype(np.int8)
z = np.random.randint(0, 6, (1, 1)).astype(np.int8)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
m = Tensor(Dtype.I8, m.shape, m.flatten())
name = "max_i8_broadcast_three_tensors"
make_test([x, y, z], m, "TensorTrait::max(array![input_0, input_1, input_2].span())", name)
default()
broadcast()
@staticmethod
def max_fp8x23_three_tensors():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
m = Tensor(Dtype.FP8x23, m.shape, to_fp(
m.flatten(), FixedImpl.FP8x23))
name = "max_fp8x23_three_tensors"
make_test([x, y, z], m, "TensorTrait::max(array![input_0, input_1, input_2].span())", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.random.randint(-3, 3, (1, 1)).astype(np.float64)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
m = Tensor(Dtype.FP8x23, m.shape, to_fp(
m.flatten(), FixedImpl.FP8x23))
name = "max_fp8x23_broadcast_three_tensors"
make_test([x, y, z], m, "TensorTrait::max(array![input_0, input_1, input_2].span())", name)
default()
broadcast()
@staticmethod
def max_fp16x16_three_tensors():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
m = Tensor(Dtype.FP16x16, m.shape, to_fp(
m.flatten(), FixedImpl.FP16x16))
name = "max_fp16x16_three_tensors"
make_test([x, y, z], m, "TensorTrait::max(array![input_0, input_1, input_2].span())", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.random.randint(-3, 3, (1, 1)).astype(np.float64)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
m = Tensor(Dtype.FP16x16, m.shape, to_fp(
m.flatten(), FixedImpl.FP16x16))
name = "max_fp16x16_broadcast_three_tensors"
make_test([x, y, z], m, "TensorTrait::max(array![input_0, input_1, input_2].span())", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/min.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
class Min(RunAll):
@staticmethod
def min_u32_two_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.minimum(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "min_u32_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.uint32)
y = np.random.randint(0, 6, (1, 2)).astype(np.uint32)
z = np.minimum(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "min_u32_broadcast_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def min_i32_two_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32)
z = np.minimum(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "min_i32_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.int32)
y = np.random.randint(0, 6, (1, 2)).astype(np.int32)
z = np.minimum(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "min_i32_broadcast_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def min_i8_two_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
z = np.minimum(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name = "min_i8_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.int8)
y = np.random.randint(0, 6, (1, 2)).astype(np.int8)
z = np.minimum(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name = "min_i8_broadcast_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def min_fp8x23_two_tensors():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.minimum(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "min_fp8x23_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.minimum(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "min_fp8x23_broadcast_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def min_fp16x16_two_tensors():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.minimum(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "min_fp16x16_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.minimum(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "min_fp16x16_broadcast_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def min_u32_three_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
m = Tensor(Dtype.U32, m.shape, m.flatten())
name = "min_u32_three_tensors"
make_test([x, y, z], m, "TensorTrait::min(array![input_0, input_1, input_2].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.uint32)
y = np.random.randint(0, 6, (1, 2)).astype(np.uint32)
z = np.random.randint(0, 6, (1, 1)).astype(np.uint32)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
m = Tensor(Dtype.U32, m.shape, m.flatten())
name = "min_u32_broadcast_three_tensors"
make_test([x, y, z], m, "TensorTrait::min(array![input_0, input_1, input_2].span())", name)
default()
broadcast()
@staticmethod
def min_i32_three_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32)
z = np.random.randint(0, 6, (3, 3, 3)).astype(np.int32)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
m = Tensor(Dtype.I32, m.shape, m.flatten())
name = "min_i32_three_tensors"
make_test([x, y, z], m, "TensorTrait::min(array![input_0, input_1, input_2].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.int32)
y = np.random.randint(0, 6, (1, 2)).astype(np.int32)
z = np.random.randint(0, 6, (1, 1)).astype(np.int32)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
m = Tensor(Dtype.I32, m.shape, m.flatten())
name = "min_i32_broadcast_three_tensors"
make_test([x, y, z], m, "TensorTrait::min(array![input_0, input_1, input_2].span())", name)
default()
broadcast()
@staticmethod
def min_i8_three_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
z = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
m = Tensor(Dtype.I8, m.shape, m.flatten())
name = "min_i8_three_tensors"
make_test([x, y, z], m, "TensorTrait::min(array![input_0, input_1, input_2].span())", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.int8)
y = np.random.randint(0, 6, (1, 2)).astype(np.int8)
z = np.random.randint(0, 6, (1, 1)).astype(np.int8)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
m = Tensor(Dtype.I8, m.shape, m.flatten())
name = "min_i8_broadcast_three_tensors"
make_test([x, y, z], m, "TensorTrait::min(array![input_0, input_1, input_2].span())", name)
default()
broadcast()
@staticmethod
def min_fp8x23_three_tensors():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
m = Tensor(Dtype.FP8x23, m.shape, to_fp(
m.flatten(), FixedImpl.FP8x23))
name = "min_fp8x23_three_tensors"
make_test([x, y, z], m, "TensorTrait::min(array![input_0, input_1, input_2].span())", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.random.randint(-3, 3, (1, 1)).astype(np.float64)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
m = Tensor(Dtype.FP8x23, m.shape, to_fp(
m.flatten(), FixedImpl.FP8x23))
name = "min_fp8x23_broadcast_three_tensors"
make_test([x, y, z], m, "TensorTrait::min(array![input_0, input_1, input_2].span())", name)
default()
broadcast()
@staticmethod
def min_fp16x16_three_tensors():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
m = Tensor(Dtype.FP16x16, m.shape, to_fp(
m.flatten(), FixedImpl.FP16x16))
name = "min_fp16x16_three_tensors"
make_test([x, y, z], m, "TensorTrait::min(array![input_0, input_1, input_2].span())", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.random.randint(-3, 3, (1, 1)).astype(np.float64)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
m = Tensor(Dtype.FP16x16, m.shape, to_fp(
m.flatten(), FixedImpl.FP16x16))
name = "min_fp16x16_broadcast_three_tensors"
make_test([x, y, z], m, "TensorTrait::min(array![input_0, input_1, input_2].span())", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/mul.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Mul(RunAll):
@staticmethod
def mul_u32():
def default():
x = np.random.randint(3, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 3, (3, 3, 3)).astype(np.uint32)
z = x * y
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "mul_u32"
make_test([x, y], z, "input_0 * input_1", name)
def broadcast():
x = np.random.randint(3, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 3, (1, 3, 1)).astype(np.uint32)
z = x * y
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "mul_u32_broadcast"
make_test([x, y], z, "input_0 * input_1", name)
default()
broadcast()
@staticmethod
def mul_i32():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
z = x * y
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "mul_i32"
make_test([x, y], z, "input_0 * input_1", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.int32)
z = x * y
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "mul_i32_broadcast"
make_test([x, y], z, "input_0 * input_1", name)
default()
broadcast()
@staticmethod
def mul_i8():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
z = x * y
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name = "mul_i8"
make_test([x, y], z, "input_0 * input_1", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.int8)
z = x * y
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name = "mul_i8_broadcast"
make_test([x, y], z, "input_0 * input_1", name)
default()
broadcast()
@staticmethod
def mul_fp8x23():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = x * y
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "mul_fp8x23"
make_test([x, y], z, "input_0 * input_1", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.float64)
z = x * y
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "mul_fp8x23_broadcast"
make_test([x, y], z, "input_0 * input_1", name)
default()
broadcast()
@staticmethod
def mul_fp16x16():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = x * y
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "mul_fp16x16"
make_test([x, y], z, "input_0 * input_1", name)
def broadcast():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 3, 1)).astype(np.float64)
z = x * y
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "mul_fp16x16_broadcast"
make_test([x, y], z, "input_0 * input_1", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/neg.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Neg(RunAll):
@staticmethod
def neg_i32():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int32)
y = np.negative(x)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "neg_i32"
make_test([x], y, "input_0.neg()", name)
@staticmethod
def neg_i8():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int8)
y = np.negative(x)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "neg_i8"
make_test([x], y, "input_0.neg()", name)
@staticmethod
def neg_fp8x23():
x = to_fp(np.random.randint(-127, 127, (2, 2)
).astype(np.int64), FixedImpl.FP8x23)
y = np.negative(x)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "neg_fp8x23"
make_test([x], y, "input_0.neg()", name)
@staticmethod
def neg_fp16x16():
x = to_fp(np.random.randint(-127, 127, (2, 2)
).astype(np.int64), FixedImpl.FP16x16)
y = np.negative(x)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "neg_fp16x16"
make_test([x], y, "input_0.neg()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/nonzero.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Nonzero(RunAll):
@staticmethod
def nonzero_u32():
def nonzero_2D():
x = np.random.randint(0, 255, (2, 4)).astype(np.uint32)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "nonzero_u32_2d"
make_test(
[x], y, "input_0.nonzero()", name)
def nonzero_3D():
x = np.random.randint(0, 255, (20, 10, 5)).astype(np.uint32)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "nonzero_u32_3d"
make_test(
[x], y, "input_0.nonzero()", name)
nonzero_2D()
nonzero_3D()
@staticmethod
def nonzero_i32():
def nonzero_2D():
x = np.random.randint(-127, 127, (2, 4)).astype(np.int32)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "nonzero_i32_2d"
make_test(
[x], y, "input_0.nonzero()", name)
def nonzero_3D():
x = np.random.randint(-127, 127, (20, 10, 5)).astype(np.int32)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "nonzero_i32_3d"
make_test(
[x], y, "input_0.nonzero()", name)
nonzero_2D()
nonzero_3D()
@staticmethod
def nonzero_i8():
def nonzero_2D():
x = np.random.randint(-127, 127, (2, 4)).astype(np.int8)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "nonzero_i8_2d"
make_test(
[x], y, "input_0.nonzero()", name)
def nonzero_3D():
x = np.random.randint(-127, 127, (20, 10, 5)).astype(np.int8)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "nonzero_i8_3d"
make_test(
[x], y, "input_0.nonzero()", name)
nonzero_2D()
nonzero_3D()
@staticmethod
def nonzero_fp8x23():
def nonzero_2D():
x = to_fp(np.random.randint(-127, 127, (2, 4)
).astype(np.int64), FixedImpl.FP8x23)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "nonzero_fp8x23_2d"
make_test(
[x], y, "input_0.nonzero()", name)
def nonzero_3D():
x = to_fp(np.random.randint(-127, 127, (20, 10, 5)
).astype(np.int64), FixedImpl.FP8x23)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "nonzero_fp8x23_3d"
make_test(
[x], y, "input_0.nonzero()", name)
nonzero_2D()
nonzero_3D()
@staticmethod
def nonzero_fp16x16():
def nonzero_2D():
x = to_fp(np.random.randint(-127, 127, (2, 4)
).astype(np.int64), FixedImpl.FP16x16)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "nonzero_fp16x16_2d"
make_test(
[x], y, "input_0.nonzero()", name)
def nonzero_3D():
x = to_fp(np.random.randint(-127, 127, (20, 10, 5)
).astype(np.int64), FixedImpl.FP16x16)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "nonzero_fp16x16_3d"
make_test(
[x], y, "input_0.nonzero()", name)
nonzero_2D()
nonzero_3D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/not.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_node, make_test, Tensor, Dtype
class Not(RunAll):
@staticmethod
def not_bool():
x = np.random.uniform(True, False, (1, 1)).astype(bool)
y = ~(x)
x = Tensor(Dtype.Bool, x.shape, x.flatten())
y = Tensor(Dtype.Bool, y.shape, y.flatten())
name = "not_bool"
make_node([x], [y], name)
make_test([x], y, "input_0", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/or.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Or(RunAll):
@staticmethod
def or_u32():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.logical_or(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "or_u32"
make_test([x, y], z, "input_0.or(@input_1)", name)
def broadcast():
x = np.random.randint(0, 6, (2, 2)).astype(np.uint32)
y = np.random.randint(0, 6, (1, 2)).astype(np.uint32)
z = np.logical_or(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "or_u32_broadcast"
make_test([x, y], z, "input_0.or(@input_1)", name)
default()
broadcast()
@staticmethod
def or_i32():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int32)
z = np.logical_or(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "or_i32"
make_test([x, y], z, "input_0.or(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int32)
y = np.random.randint(-3, 3, (1, 2)).astype(np.int32)
z = np.logical_or(x, y)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "or_i32_broadcast"
make_test([x, y], z, "input_0.or(@input_1)", name)
default()
broadcast()
@staticmethod
def or_i8():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
z = np.logical_or(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "or_i8"
make_test([x, y], z, "input_0.or(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int8)
y = np.random.randint(-3, 3, (1, 2)).astype(np.int8)
z = np.logical_or(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "or_i8_broadcast"
make_test([x, y], z, "input_0.or(@input_1)", name)
default()
broadcast()
@staticmethod
def or_fp8x23():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.logical_or(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "or_fp8x23"
make_test([x, y], z, "input_0.or(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.logical_or(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "or_fp8x23_broadcast"
make_test([x, y], z, "input_0.or(@input_1)", name)
default()
broadcast()
@staticmethod
def or_fp16x16():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.logical_or(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "or_fp16x16"
make_test([x, y], z, "input_0.or(@input_1)", name)
def broadcast():
x = np.random.randint(-3, 3, (2, 2)).astype(np.float64)
y = np.random.randint(-3, 3, (1, 2)).astype(np.float64)
z = np.logical_or(x, y)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.U32, z.shape, z.flatten())
name = "or_fp16x16_broadcast"
make_test([x, y], z, "input_0.or(@input_1)", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/pow.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Pow(RunAll):
@staticmethod
def pow_fp8x23():
def default():
x = np.array([1, 2, 3]).astype(np.float64)
y = np.array([1, 2, 3]).astype(np.float64)
z = np.array(pow(x, y), dtype=np.float64)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "pow_fp8x23"
make_test([x, y], z, "input_0.pow(@input_1)", name)
def broadcast():
x = np.array([1, 2, 3]).astype(np.float64)
y = np.array([2]).astype(np.float64)
z = np.array(pow(x, y), dtype=np.float64)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
z.flatten(), FixedImpl.FP8x23))
name = "pow_fp8x23_broadcast"
make_test([x, y], z, "input_0.pow(@input_1)", name)
default()
broadcast()
@staticmethod
def and_fp16x16():
def default():
x = np.array([1, 2, 3]).astype(np.float64)
y = np.array([1, 2, 3]).astype(np.float64)
z = np.array(pow(x, y), dtype=np.float64)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "pow_fp16x16"
make_test([x, y], z, "input_0.pow(@input_1)", name)
def broadcast():
x = np.array([1, 2, 3]).astype(np.float64)
y = np.array([2]).astype(np.float64)
z = np.array(pow(x, y), dtype=np.float64)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.flatten(), FixedImpl.FP16x16))
name = "pow_fp16x16_broadcast"
make_test([x, y], z, "input_0.pow(@input_1)", name)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/random_uniform_like.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def random_uniform_like(x: np.ndarray, high: int=1,low: int=0,seed: int=25) ->np.ndarray:
dtype = np.float64
if seed is None or np.isnan(seed): # type: ignore
state = np.random.RandomState()
else:
state = np.random.RandomState(seed=int(seed)) # type: ignore
res = state.rand(*x.shape).astype(dtype)
res *= high - low # type: ignore
res += low # type: ignore
return (res.astype(dtype),)
def get_data_statement(data: np.ndarray, dtype: Dtype) -> list[str]:
match dtype:
case Dtype.FP8x23:
return ["Option::Some(FP8x23 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"})" for x in data.flatten()]
case Dtype.FP16x16:
return ["Option::Some(FP16x16 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"})" for x in data.flatten()]
case Dtype.U32:
return [f"Option::Some({int(x)})" for x in data.flatten()]
class Random_uniform_like(RunAll):
@staticmethod
def fp8x23():
x = np.random.uniform(1, 10, (1, 2, 2, 4)).astype(np.float64)
y = random_uniform_like(x)
args = [10, 1]
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP8x23), Dtype.FP8x23)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y[0].shape, to_fp(
y[0].flatten(), FixedImpl.FP8x23))
name = "random_uniform_like_fp8x23"
make_test(
[x], # List of input tensors.
y, # The expected output result.
f"TensorTrait::random_uniform_like(@input_0, {','.join(args_str)}, Option::Some(354145))", # The code signature.
name # The name of the generated folder.
)
@staticmethod
def fp16x16():
x = np.random.uniform(1, 10, (1, 2, 2, 4)).astype(np.float16)
y = random_uniform_like(x)
args = [10, 1]
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP16x16), Dtype.FP16x16)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y[0].shape, to_fp(
y[0].flatten(), FixedImpl.FP16x16))
name = "random_uniform_like_fp16x16"
make_test(
[x], # List of input tensors.
y, # The expected output result.
f"TensorTrait::random_uniform_like(@input_0, {','.join(args_str)}, Option::Some(354145))", # The code signature.
name # The name of the generated folder.
)
# @staticmethod
# def fp64x64():
# x = np.random.uniform(-3, 3, (1, 2, 2, 4)).astype(np.float64)
# y = random_uniform_like(x)
# x = Tensor(Dtype.FP64x64, x.shape, to_fp(
# x.flatten(), FixedImpl.FP64x64))
# y = Tensor(Dtype.FP64x64, y[0].shape, to_fp(
# y[0].flatten(), FixedImpl.FP64x64))
# name = "random_uniform_like_fp64x64"
# make_test([x], y, "TensorTrait::random_uniform_like(@input_0, 5, 1, 10)",
# name)
# @staticmethod
# def fpi8():
# x = np.random.randint(-3, 3, (1, 2, 2, 4)).astype(np.int8)
# y = random_uniform_like(x)
# x = Tensor(Dtype.I8, x.shape, x.flatten())
# y = Tensor(Dtype.I8, y[0].shape, y[0].flatten())
# name = "random_uniform_like_i8"
# make_test([x], y, "TensorTrait::random_uniform_like(@input_0, 5, 1, 10)",
# name)
# @staticmethod
# def fpi32():
# x = np.random.randint(-3, 3, (1, 2, 2, 4)).astype(np.int32)
# y = random_uniform_like(x)
# x = Tensor(Dtype.I32, x.shape, x.flatten())
# y = Tensor(Dtype.I32, y[0].shape, y[0].flatten())
# name = "random_uniform_like_i32"
# make_test([x], y, "TensorTrait::random_uniform_like(@input_0, 5, 1, 10)",
# name)
# @staticmethod
# def fpu32():
# x = np.random.randint(-3, 3, (1, 2, 2, 4)).astype(np.uint32)
# y = random_uniform_like(x)
# args = [5, 1, 10]
# args_str = get_data_statement(np.array(args).flatten(), Dtype.U32)
# x = Tensor(Dtype.U32, x.shape, x.flatten())
# y = Tensor(Dtype.U32, y[0].shape, y[0].flatten())
# name = "random_uniform_like_u32"
# make_test(
# [x], # List of input tensors.
# y, # The expected output result.
# f"TensorTrait::random_uniform_like(@input_0, {','.join(args_str)})", # The code signature.
# name # The name of the generated folder.
# )
|
https://github.com/gizatechxyz/orion
|
nodegen/node/range.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait, get_data_statement
class Range(RunAll):
@staticmethod
# We test here with fp8x23 implementation.
def fp8x23():
args = [1, 5, 0.3]
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP8x23), Dtype.FP8x23)
y = np.arange(*args)
print(y)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
# Define the name of the generated folder.
name = "range_fp8x23"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::range({','.join(args_str)})", # The code signature.
name, # The name of the generated folder.
)
@staticmethod
# We test here with fp16x16 implementation.
def fp16x16():
args = [1, 25, 3]
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP16x16), Dtype.FP16x16)
y = np.arange(*args)
print(y)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
# Define the name of the generated folder.
name = "range_fp16x16"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::range({','.join(args_str)})", # The code signature.
name, # The name of the generated folder.
)
@staticmethod
# We test here with i8 implementation.
def i8():
args = [-1, 25, 3]
args_str = get_data_statement(np.array(args).flatten(), Dtype.I8)
y = np.arange(*args)
print(y)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.I8, y.shape, y.flatten())
# Define the name of the generated folder.
name = "range_i8"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::range({','.join(args_str)})", # The code signature.
name, # The name of the generated folder.
)
@staticmethod
# We test here with i32 implementation.
def i32():
args = [21, 2, -3]
args_str = get_data_statement(np.array(args).flatten(), Dtype.I32)
y = np.arange(*args)
print(y)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.I32, y.shape, y.flatten())
# Define the name of the generated folder.
name = "range_i32"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::range({','.join(args_str)})", # The code signature.
name, # The name of the generated folder.
)
@staticmethod
# We test here with u32 implementation.
def u32():
args = [1, 25, 3]
args_str = get_data_statement(np.array(args).flatten(), Dtype.U32)
y = np.arange(*args)
print(y)
# Convert the floats values in `y` to fixed points with `to_fp` method:
y = Tensor(Dtype.U32, y.shape, y.flatten())
# Define the name of the generated folder.
name = "range_u32"
# Invoke `make_test` method to generate corresponding Cairo tests:
make_test(
[], # List of input tensors.
y, # The expected output result.
f"TensorTrait::range({','.join(args_str)})", # The code signature.
name, # The name of the generated folder.
)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/reduce_l1.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
import numpy as np
class Reduce_l1(RunAll):
@staticmethod
def reduce_l1_fp8x23():
def reduce_l1_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=False).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_l1_fp8x23_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_l1(2, false)", name)
def reduce_l1_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=True).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_l1_fp8x23_export_keepdims"
make_test(
[x], y, "input_0.reduce_l1(2, true)", name)
def reduce_l1_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=True).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_l1_fp8x23_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_l1(0, true)", name)
reduce_l1_export_do_not_keepdims()
reduce_l1_export_keepdims()
reduce_l1_axis_0()
@staticmethod
def reduce_l1_fp16x16():
def reduce_l1_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=False).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reduce_l1_fp16x16_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_l1(2, false)", name)
def reduce_l1_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=True).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reduce_l1_fp16x16_export_keepdims"
make_test(
[x], y, "input_0.reduce_l1(2, true)", name)
def reduce_l1_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=True).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reduce_l1_fp16x16_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_l1(0, true)", name)
reduce_l1_export_do_not_keepdims()
reduce_l1_export_keepdims()
reduce_l1_axis_0()
@staticmethod
def reduce_l1_i8():
def reduce_l1_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int8)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int8)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=False).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "reduce_l1_i8_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_l1(2, false)", name)
def reduce_l1_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int8)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int8)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=True).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "reduce_l1_i8_export_keepdims"
make_test(
[x], y, "input_0.reduce_l1(2, true)", name)
def reduce_l1_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.int8)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int8)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=True).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "reduce_l1_i8_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_l1(0, true)", name)
reduce_l1_export_do_not_keepdims()
reduce_l1_export_keepdims()
reduce_l1_axis_0()
@staticmethod
def reduce_l1_i32():
def reduce_l1_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int32)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int32)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=False).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_l1_i32_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_l1(2, false)", name)
def reduce_l1_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int32)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int32)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_l1_i32_export_keepdims"
make_test(
[x], y, "input_0.reduce_l1(2, true)", name)
def reduce_l1_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.int32)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int32)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_l1_i32_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_l1(0, true)", name)
reduce_l1_export_do_not_keepdims()
reduce_l1_export_keepdims()
reduce_l1_axis_0()
@staticmethod
def reduce_l1_u32():
def reduce_l1_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.uint32)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.uint32)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=False).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_l1_u32_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_l1(2, false)", name)
def reduce_l1_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.uint32)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.uint32)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_l1_u32_export_keepdims"
make_test(
[x], y, "input_0.reduce_l1(2, true)", name)
def reduce_l1_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.uint32)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.uint32)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_l1_u32_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_l1(0, true)", name)
reduce_l1_export_do_not_keepdims()
reduce_l1_export_keepdims()
reduce_l1_axis_0()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/reduce_l2.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_node, make_test, to_fp, Tensor, Dtype, FixedImpl
import numpy as np
class Reduce_l2(RunAll):
@staticmethod
def reduce_l2_fp8x23():
def reduce_l2_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sqrt(np.sum(a=np.square(x), axis=tuple(axes), keepdims=False)).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_l2_fp8x23_export_do_not_keepdims"
make_node([x], [y], name)
make_test(
[x], y, "input_0.reduce_l2(2, false)", name)
def reduce_l2_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sqrt(np.sum(a=np.square(x), axis=tuple(axes), keepdims=True)).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_l2_fp8x23_export_keepdims"
make_node([x], [y], name)
make_test(
[x], y, "input_0.reduce_l2(2, true)", name)
def reduce_l2_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sqrt(np.sum(a=np.square(x), axis=tuple(axes), keepdims=True)).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_l2_fp8x23_export_negative_axes_keepdims"
make_node([x], [y], name)
make_test(
[x], y, "input_0.reduce_l2(0, true)", name)
reduce_l2_export_do_not_keepdims()
reduce_l2_export_keepdims()
reduce_l2_axis_0()
@staticmethod
def reduce_l2_fp16x16():
def reduce_l2_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sqrt(np.sum(a=np.square(x), axis=tuple(axes), keepdims=False)).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reduce_l2_fp16x16_export_do_not_keepdims"
make_node([x], [y], name)
make_test(
[x], y, "input_0.reduce_l2(2, false)", name)
def reduce_l2_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sqrt(np.sum(a=np.square(x), axis=tuple(axes), keepdims=True)).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reduce_l2_fp16x16_export_keepdims"
make_node([x], [y], name)
make_test(
[x], y, "input_0.reduce_l2(2, true)", name)
def reduce_l2_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sqrt(np.sum(a=np.square(x), axis=tuple(axes), keepdims=True)).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reduce_l2_fp16x16_export_negative_axes_keepdims"
make_node([x], [y], name)
make_test(
[x], y, "input_0.reduce_l2(0, true)", name)
reduce_l2_export_do_not_keepdims()
reduce_l2_export_keepdims()
reduce_l2_axis_0()
@staticmethod
def reduce_l2_complex64():
def reduce_l2_axis_0():
shape = [2, 3]
axes = np.array([0], dtype=np.int64)
keepdims = True
x = np.reshape(np.array([1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j, 4.- 1.j]), shape)
y = np.sqrt(np.sum(a=np.square(abs(x)), axis=tuple(axes), keepdims=True))
print(to_fp(x.flatten(), FixedImpl.FP64x64))
x = Tensor(Dtype.COMPLEX64, x.shape, to_fp(
x.flatten(), FixedImpl.FP64x64))
y = Tensor(Dtype.COMPLEX64, y.shape, to_fp(
y.flatten(), FixedImpl.FP64x64))
name = "reduce_l2_complex64_axis_0"
make_test(
[x], y, "input_0.reduce_l2(0, true)", name)
reduce_l2_axis_0()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/reduce_log_sum.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Reduce_log_sum(RunAll):
@staticmethod
def reduce_log_sum_fp8x23():
def reduce_log_sum_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape)
y = np.log(np.sum(x, axis=tuple(axes), keepdims=False))
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_log_sum_fp8x23_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum(2, false)", name)
def reduce_log_sum_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.log(np.sum(x, axis=tuple(axes), keepdims=True))
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_log_sum_fp8x23_export_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum(2, true)", name)
def reduce_log_sum_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1), shape)
y = np.log(np.sum(x, axis=tuple(axes), keepdims=True))
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_log_sum_fp8x23_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum(0, true)", name)
reduce_log_sum_export_do_not_keepdims()
reduce_log_sum_export_keepdims()
reduce_log_sum_axis_0()
@staticmethod
def reduce_log_sum_fp16x16():
def reduce_log_sum_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.log(np.sum(x, axis=tuple(axes), keepdims=False))
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_log_sum_fp16x16_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum(2, false)", name)
def reduce_log_sum_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.log(np.sum(x, axis=tuple(axes), keepdims=True))
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_log_sum_fp16x16_export_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum(2, true)", name)
def reduce_log_sum_axis_0():
shape = [2, 2, 2]
axes = np.array([0], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.log(np.sum(x, axis=tuple(axes), keepdims=True))
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_log_sum_fp16x16_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum(0, true)", name)
reduce_log_sum_export_do_not_keepdims()
reduce_log_sum_export_keepdims()
reduce_log_sum_axis_0()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/reduce_log_sum_exp.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, Tensor, Dtype, FixedImpl, to_fp
class Reduce_log_sum_exp(RunAll):
@staticmethod
def reduce_log_sum_exp_fp32x32():
def reduce_log_sum_exp_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1), shape)
y = np.log(np.sum(np.exp(x), axis=tuple(axes), keepdims=False)).astype(np.float64)
x = Tensor(Dtype.FP32x32, x.shape, to_fp(
x.flatten(), FixedImpl.FP32x32))
y = Tensor(Dtype.FP32x32, y.shape, to_fp(
y.flatten(), FixedImpl.FP32x32))
name = "reduce_log_sum_exp_fp32x32_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum_exp(2, false)", name)
def reduce_log_sum_exp_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1), shape)
y = np.log(np.sum(np.exp(x), axis=tuple(axes), keepdims=True)).astype(np.float64)
x = Tensor(Dtype.FP32x32, x.shape, to_fp(
x.flatten(), FixedImpl.FP32x32))
y = Tensor(Dtype.FP32x32, y.shape, to_fp(
y.flatten(), FixedImpl.FP32x32))
name = "reduce_log_sum_exp_fp32x32_export_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum_exp(2, true)", name)
def reduce_log_sum_exp_axis_0():
shape = [3, 2, 2]
axes = np.array([0], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1), shape)
y = np.log(np.sum(np.exp(x), axis=tuple(axes), keepdims=True)).astype(np.float64)
x = Tensor(Dtype.FP32x32, x.shape, to_fp(
x.flatten(), FixedImpl.FP32x32))
y = Tensor(Dtype.FP32x32, y.shape, to_fp(
y.flatten(), FixedImpl.FP32x32))
name = "reduce_log_sum_exp_fp32x32_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum_exp(0, true)", name)
reduce_log_sum_exp_export_do_not_keepdims()
reduce_log_sum_exp_export_keepdims()
reduce_log_sum_exp_axis_0()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/reduce_mean.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Reduce_mean(RunAll):
@staticmethod
def reduce_mean_u32():
def reduce_mean_1D():
x = np.array([0, 1, 2,]).astype(np.uint32)
y = np.mean(x, keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_mean_u32_1D"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::None(()), Option::None(()))", name)
def reduce_mean_2D():
def default():
x = np.array([0, 1, 2, 3]).astype(np.uint32).reshape(2, 2)
y = np.mean(x, keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_mean_u32_2D_default"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::None(()), Option::None(()))", name)
def keepdims():
x = np.array([0, 1, 2, 3]).astype(np.uint32).reshape(2, 2)
y = np.mean(x, keepdims=False).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_mean_u32_2D_keepdims"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.uint32).reshape(2, 2)
y = np.mean(x, axis=(1), keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_mean_u32_2D_axis_1"
make_test(
[x], y, "input_0.reduce_mean(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_mean_1D()
reduce_mean_2D()
@staticmethod
def reduce_mean_i32():
def reduce_mean_1D():
x = np.array([0, 1, 2,]).astype(np.int32)
y = np.mean(x, keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_mean_i32_1D"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::None(()), Option::None(()))", name)
def reduce_mean_2D():
def default():
x = np.array([0, 1, 2, 3]).astype(np.int32).reshape(2, 2)
y = np.mean(x, keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_mean_i32_2D_default"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::None(()), Option::None(()))", name)
def keepdims():
x = np.array([0, 1, 2, 3]).astype(np.int32).reshape(2, 2)
y = np.mean(x, keepdims=False).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_mean_i32_2D_keepdims"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.int32).reshape(2, 2)
y = np.mean(x, axis=(1), keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_mean_i32_2D_axis_1"
make_test(
[x], y, "input_0.reduce_mean(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_mean_1D()
reduce_mean_2D()
@staticmethod
def reduce_mean_i8():
def reduce_mean_1D():
x = np.array([0, 1, 2,]).astype(np.int8)
y = np.mean(x, keepdims=True).astype(np.int8)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_mean_i8_1D"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::None(()), Option::None(()))", name)
def reduce_mean_2D():
def default():
x = np.array([0, 1, 2, 3]).astype(np.int8).reshape(2, 2)
y = np.mean(x, keepdims=True).astype(np.int8)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_mean_i8_2D_default"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::None(()), Option::None(()))", name)
def keepdims():
x = np.array([0, 1, 2, 3]).astype(np.int8).reshape(2, 2)
y = np.mean(x, keepdims=False).astype(np.int8)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_mean_i8_2D_keepdims"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.int8).reshape(2, 2)
y = np.mean(x, axis=(1), keepdims=True).astype(np.int8)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_mean_i8_2D_axis_1"
make_test(
[x], y, "input_0.reduce_mean(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_mean_1D()
reduce_mean_2D()
@staticmethod
def reduce_mean_fp8x23():
def reduce_mean_1D():
x = np.array([0, 1, 2,]).astype(np.int64)
y = np.mean(x, keepdims=True)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_mean_fp8x23_1D"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::None(()), Option::None(()))", name)
def reduce_mean_2D():
def default():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.mean(x, keepdims=True)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_mean_fp8x23_2D_default"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::None(()), Option::None(()))", name)
def keepdims():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.mean(x, keepdims=False)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_mean_fp8x23_2D_keepdims"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.mean(x, axis=(1), keepdims=True)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_mean_fp8x23_2D_axis_1"
make_test(
[x], y, "input_0.reduce_mean(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_mean_1D()
reduce_mean_2D()
@staticmethod
def reduce_mean_fp16x16():
def reduce_mean_1D():
x = np.array([0, 1, 2,]).astype(np.int64)
y = np.mean(x, keepdims=True)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "reduce_mean_fp16x16_1D"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::None(()), Option::None(()))", name)
def reduce_mean_2D():
def default():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.mean(x, keepdims=True)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "reduce_mean_fp16x16_2D_default"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::None(()), Option::None(()))", name)
def keepdims():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.mean(x, keepdims=False)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "reduce_mean_fp16x16_2D_keepdims"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.mean(x, axis=(1), keepdims=True)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "reduce_mean_fp16x16_2D_axis_1"
make_test(
[x], y, "input_0.reduce_mean(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_mean_1D()
reduce_mean_2D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/reduce_min.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Reduce_min(RunAll):
@staticmethod
def reduce_min_u32():
def reduce_min_1D():
x = np.array([0, 1, 2,]).astype(np.uint32)
y = np.minimum.reduce(x, axis=None, keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_min_u32_1D"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::None(()), Option::None(()))", name)
def reduce_min_2D():
def default():
x = np.array([0, 1, 2, 3]).astype(np.uint32).reshape(2, 2)
y = np.minimum.reduce(x, axis=None, keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_min_u32_2D_default"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::None(()), Option::None(()))", name)
def keepdims():
x = np.array([0, 1, 2, 3]).astype(np.uint32).reshape(2, 2)
y = np.minimum.reduce(x, axis=None, keepdims=False).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_min_u32_2D_keepdims"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.uint32).reshape(2, 2)
y = np.minimum.reduce(x, axis=(1), keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_min_u32_2D_axis_1"
make_test(
[x], y, "input_0.reduce_min(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_min_1D()
reduce_min_2D()
@staticmethod
def reduce_min_i32():
def reduce_min_1D():
x = np.array([0, 1, 2,]).astype(np.int32)
y = np.minimum.reduce(x, axis=None, keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_min_i32_1D"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::None(()), Option::None(()))", name)
def reduce_min_2D():
def default():
x = np.array([0, 1, 2, 3]).astype(np.int32).reshape(2, 2)
y = np.minimum.reduce(x, axis=None, keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_min_i32_2D_default"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::None(()), Option::None(()))", name)
def keepdims():
x = np.array([0, 1, 2, 3]).astype(np.int32).reshape(2, 2)
y = np.minimum.reduce(x, axis=None, keepdims=False).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_min_i32_2D_keepdims"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.int32).reshape(2, 2)
y = np.minimum.reduce(x, axis=(1), keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_min_i32_2D_axis_1"
make_test(
[x], y, "input_0.reduce_min(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_min_1D()
reduce_min_2D()
@staticmethod
def reduce_min_i8():
def reduce_min_1D():
x = np.array([0, 1, 2,]).astype(np.int8)
y = np.minimum.reduce(x, axis=None, keepdims=True).astype(np.int8)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_min_i8_1D"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::None(()), Option::None(()))", name)
def reduce_min_2D():
def default():
x = np.array([0, 1, 2, 3]).astype(np.int8).reshape(2, 2)
y = np.minimum.reduce(x, axis=None, keepdims=True).astype(np.int8)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_min_i8_2D_default"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::None(()), Option::None(()))", name)
def keepdims():
x = np.array([0, 1, 2, 3]).astype(np.int8).reshape(2, 2)
y = np.minimum.reduce(x, axis=None, keepdims=False).astype(np.int8)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_min_i8_2D_keepdims"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.int8).reshape(2, 2)
y = np.minimum.reduce(x, axis=(1), keepdims=True).astype(np.int8)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_min_i8_2D_axis_1"
make_test(
[x], y, "input_0.reduce_min(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_min_1D()
reduce_min_2D()
@staticmethod
def reduce_min_fp8x23():
def reduce_min_1D():
x = np.array([0, 1, 2,]).astype(np.int64)
y = np.minimum.reduce(x, axis=None, keepdims=True)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_min_fp8x23_1D"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::None(()), Option::None(()))", name)
def reduce_min_2D():
def default():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.minimum.reduce(x, axis=None, keepdims=True)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_min_fp8x23_2D_default"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::None(()), Option::None(()))", name)
def keepdims():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.minimum.reduce(x, axis=None, keepdims=False)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_min_fp8x23_2D_keepdims"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.minimum.reduce(x, axis=(1), keepdims=True)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_min_fp8x23_2D_axis_1"
make_test(
[x], y, "input_0.reduce_min(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_min_1D()
reduce_min_2D()
@staticmethod
def reduce_min_fp16x16():
def reduce_min_1D():
x = np.array([0, 1, 2,]).astype(np.int64)
y = np.minimum.reduce(x, axis=None, keepdims=True)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "reduce_min_fp16x16_1D"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::None(()), Option::None(()))", name)
def reduce_min_2D():
def default():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.minimum.reduce(x, axis=None, keepdims=True)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "reduce_min_fp16x16_2D_default"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::None(()), Option::None(()))", name)
def keepdims():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.minimum.reduce(x, axis=None, keepdims=False)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "reduce_min_fp16x16_2D_keepdims"
make_test(
[x], y, "input_0.reduce_min(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.minimum.reduce(x, axis=(1), keepdims=True)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "reduce_min_fp16x16_2D_axis_1"
make_test(
[x], y, "input_0.reduce_min(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_min_1D()
reduce_min_2D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/reduce_sum.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Reduce_sum(RunAll):
@staticmethod
def reduce_sum_no_keep_dims():
axes = np.array([1], dtype=np.uint32)
keepdims = 0
x = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [
[9, 10], [11, 12]]]).astype(np.uint32)
y = np.sum(x, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_sum_no_keep_dims"
make_test(
[x], y, "input_0.reduce_sum(Option::Some(array![1].span()), Option::Some(false), Option::None)", name)
@staticmethod
def reduce_sum_keep_dims():
axes = np.array([1], dtype=np.uint32)
keepdims = 1
x = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [
[9, 10], [11, 12]]]).astype(np.uint32)
y = np.sum(x, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_sum_keep_dims"
make_test(
[x], y, "input_0.reduce_sum(Option::Some(array![1].span()), Option::Some(true), Option::None)", name)
@staticmethod
def reduce_sum_default_axes_keepdims():
keepdims = 1
x = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [
[9, 10], [11, 12]]]).astype(np.uint32)
y = np.sum(x, axis=None, keepdims=keepdims == 1)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_sum_default_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum(Option::Some(array![].span()), Option::Some(true), Option::None)", name)
@staticmethod
def reduce_sum_negative_axes_keepdims():
axes = np.array([-2], dtype=np.int64)
keepdims = 1
x = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [
[9, 10], [11, 12]]]).astype(np.uint32)
y = np.sum(x, axis=tuple(axes.tolist()), keepdims=keepdims == 1)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_sum_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum(Option::Some(array![-2].span()), Option::Some(true), Option::None)", name)
@staticmethod
def reduce_sum_empty_axes_input_noop():
x = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [
[9, 10], [11, 12]]]).astype(np.uint32)
y = np.array(x)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_sum_empty_axes_input_noop"
make_test(
[x], y, "input_0.reduce_sum(Option::None, Option::Some(true), Option::Some(true))", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/reduce_sum_square.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
import numpy as np
class Reduce_sum_square(RunAll):
@staticmethod
def reduce_sum_square_fp8x23():
def reduce_sum_square_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=False).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_sum_square_fp8x23_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(2, false)", name)
def reduce_sum_square_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=True).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_sum_square_fp8x23_export_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(2, true)", name)
def reduce_sum_square_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=True).astype(np.int64)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "reduce_sum_square_fp8x23_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(0, true)", name)
reduce_sum_square_export_do_not_keepdims()
reduce_sum_square_export_keepdims()
reduce_sum_square_axis_0()
@staticmethod
def reduce_sum_square_fp16x16():
def reduce_sum_square_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=False).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reduce_sum_square_fp16x16_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(2, false)", name)
def reduce_sum_square_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=True).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reduce_sum_square_fp16x16_export_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(2, true)", name)
def reduce_sum_square_axis_0():
shape = [2, 2, 2]
axes = np.array([0], dtype=np.int64)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int64)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=True).astype(np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reduce_sum_square_fp16x16_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(0, true)", name)
reduce_sum_square_export_do_not_keepdims()
reduce_sum_square_export_keepdims()
reduce_sum_square_axis_0()
@staticmethod
def reduce_sum_square_i8():
def reduce_sum_square_export_do_not_keepdims():
shape = [2, 2, 2]
axes = np.array([2], dtype=np.int8)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int8)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=False).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "reduce_sum_square_i8_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(2, false)", name)
def reduce_sum_square_export_keepdims():
shape = [2, 2, 2]
axes = np.array([2], dtype=np.int8)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int8)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=True).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "reduce_sum_square_i8_export_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(2, true)", name)
def reduce_sum_square_axis_0():
shape = [2, 2, 2]
axes = np.array([0], dtype=np.int8)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int8)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=True).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "reduce_sum_square_i8_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(0, true)", name)
reduce_sum_square_export_do_not_keepdims()
reduce_sum_square_export_keepdims()
reduce_sum_square_axis_0()
@staticmethod
def reduce_sum_square_i32():
def reduce_sum_square_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int32)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int32)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=False).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_sum_square_i32_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(2, false)", name)
def reduce_sum_square_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int32)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int32)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_sum_square_i32_export_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(2, true)", name)
def reduce_sum_square_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.int32)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int32)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reduce_sum_square_i32_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(0, true)", name)
reduce_sum_square_export_do_not_keepdims()
reduce_sum_square_export_keepdims()
reduce_sum_square_axis_0()
@staticmethod
def reduce_sum_square_u32():
def reduce_sum_square_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.uint32)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.uint32)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=False).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_sum_square_u32_export_do_not_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(2, false)", name)
def reduce_sum_square_export_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.uint32)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.uint32)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_sum_square_u32_export_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(2, true)", name)
def reduce_sum_square_axis_0():
shape = [3, 3, 3]
axes = np.array([0], dtype=np.uint32)
keepdims = True
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.uint32)
y = np.sum(a=np.square(x), axis=tuple(axes), keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_sum_square_u32_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(0, true)", name)
reduce_sum_square_export_do_not_keepdims()
reduce_sum_square_export_keepdims()
reduce_sum_square_axis_0()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/relu.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, Trait, FixedImpl
import tensorflow as tf
class Relu(RunAll):
@staticmethod
def relu_i32():
x = np.random.randint(-5, 9, (2, 2)).astype(np.int32)
layer = tf.keras.layers.ReLU()
y = layer(x).numpy()
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "relu_i32"
make_test([x], y, "NNTrait::relu(@input_0)",
name, Trait.NN)
@staticmethod
def relu_i8():
x = np.random.randint(-5, 9, (2, 2)).astype(np.int8)
layer = tf.keras.layers.ReLU()
y = layer(x).numpy()
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "relu_i8"
make_test([x], y, "NNTrait::relu(@input_0)",
name, Trait.NN)
@staticmethod
def relu_fp8x23():
x = np.random.uniform(-5, 7, (2, 2)).astype(np.float64)
layer = tf.keras.layers.ReLU()
y = layer(x).numpy()
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "relu_fp8x23"
make_test([x], y, "NNTrait::relu(@input_0)",
name, Trait.NN)
@staticmethod
def relu_fp16x16():
x = np.random.uniform(-5, 7, (2, 2)).astype(np.float64)
layer = tf.keras.layers.ReLU()
y = layer(x).numpy()
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "relu_fp16x16"
make_test([x], y, "NNTrait::relu(@input_0)",
name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/reshape.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, Tensor, Dtype
original_shape = [2, 3, 4]
data = np.random.random_sample(original_shape).astype(np.int32)
def reshape_reference_implementation(
data: np.ndarray, shape: np.ndarray, allowzero: int = 0
) -> np.ndarray:
# replace zeros with corresponding dim size
# we need to do this because np.reshape doesn't support 0 by default unless 'allowzero' is set
new_shape = np.copy(shape)
if allowzero == 0:
zeros_index = np.where(shape == 0)
new_shape[zeros_index] = np.array(data.shape)[zeros_index]
reshaped = np.reshape(data, new_shape)
return reshaped
class Reshape(RunAll):
@staticmethod
def reshape_reordered_all_dims():
y = reshape_reference_implementation(
data, np.array([4, 2, 3], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_reordered_all_dims"
make_test([x], y, "input_0.reshape(array![4,2,3].span(), false)", name)
@staticmethod
def reshape_reordered_last_dims():
y = reshape_reference_implementation(
data, np.array([2, 4, 3], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_reordered_last_dims"
make_test([x], y, "input_0.reshape(array![2,4,3].span(), false)", name)
@staticmethod
def reshape_reduced_dims():
y = reshape_reference_implementation(
data, np.array([2, 12], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_reduced_dims"
make_test([x], y, "input_0.reshape(array![2,12].span(), false)", name)
@staticmethod
def reshape_extended_dims():
y = reshape_reference_implementation(
data, np.array([2, 3, 2, 2], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_extended_dims"
make_test([x], y, "input_0.reshape(array![2, 3, 2, 2].span(), false)", name)
@staticmethod
def reshape_one_dim():
y = reshape_reference_implementation(
data, np.array([24], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_one_dim"
make_test([x], y, "input_0.reshape(array![24].span(), false)", name)
@staticmethod
def reshape_negative_dim():
y = reshape_reference_implementation(
data, np.array([2, -1, 2], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_negative_dim"
make_test([x], y, "input_0.reshape(array![2, -1, 2].span(), false)", name)
@staticmethod
def reshape_negative_extended_dims():
y = reshape_reference_implementation(
data, np.array([-1, 2, 3, 4], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_negative_extended_dims"
make_test([x], y, "input_0.reshape(array![-1, 2, 3, 4].span(), false)", name)
@staticmethod
def reshape_zero_dim():
y = reshape_reference_implementation(
data, np.array([2, 0, 4, 1], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_zero_dim"
make_test([x], y, "input_0.reshape(array![2, 0, 4, 1].span(), false)", name)
@staticmethod
def reshape_zero_and_negative_dim():
y = reshape_reference_implementation(
data, np.array([2, 0, 1, -1], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_zero_and_negative_dim"
make_test([x], y, "input_0.reshape(array![2, 0, 1, -1].span(), false)", name)
@staticmethod
def reshape_zero_and_negative_dim():
original_shape = [0, 3, 4]
data = np.random.random_sample(original_shape).astype(np.int32)
y = reshape_reference_implementation(
data, np.array([3, 4, 0], dtype=np.int64), allowzero=1)
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_zero_and_negative_dim"
make_test([x], y, "input_0.reshape(array![3, 4, 0].span(), true)", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/resize.py
|
# Python test implementation from ONNX library : https://github.com/onnx/onnx/blob/main/onnx/reference/ops/op_resize.py
import numpy as np
from typing import Any, Callable
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
def _cartesian(arrays: list[np.ndarray], out: np.ndarray | None = None) -> np.ndarray:
#From https://stackoverflow.com/a/1235363
arrays = [np.asarray(x) for x in arrays]
dtype = arrays[0].dtype
n = np.prod([x.size for x in arrays])
if out is None:
out = np.zeros([n, len(arrays)], dtype=dtype)
m = n // arrays[0].size
out[:, 0] = np.repeat(arrays[0], m)
if arrays[1:]:
_cartesian(arrays[1:], out=out[0:m, 1:])
for j in range(1, arrays[0].size):
out[j * m : (j + 1) * m, 1:] = out[0:m, 1:]
return out
def _get_neighbor_idxes(x: float, n: int, limit: int) -> np.ndarray:
idxes = sorted(range(limit), key=lambda idx: (abs(x - idx), idx))[:n]
idxes = sorted(idxes)
return np.array(idxes)
def _get_neighbor(x: float, n: int, data: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
pad_width = np.ceil(n / 2).astype(int)
padded = np.pad(data, pad_width, mode="edge")
x += pad_width
idxes = _get_neighbor_idxes(x, n, len(padded))
ret = padded[idxes]
return idxes - pad_width, ret
def linear_coeffs(ratio: float, scale: float | None = None) -> np.ndarray:
del scale
return np.array([1 - ratio, ratio])
def linear_coeffs_antialias(ratio: float, scale: float) -> np.ndarray:
scale = min(scale, 1.0)
start = int(np.floor(-1 / scale) + 1)
footprint = 2 - 2 * start
args = (np.arange(start, start + footprint) - ratio) * scale
coeffs = np.clip(1 - np.abs(args), 0, 1)
return np.array(coeffs) / sum(coeffs)
def cubic_coeffs_antialias(ratio: float, scale: float, A: float = -0.75) -> np.ndarray:
scale = min(scale, 1.0)
def compute_coeff(x: float) -> float:
x = abs(x)
x_2 = x * x
x_3 = x * x_2
if x <= 1:
return (A + 2) * x_3 - (A + 3) * x_2 + 1
if x < 2:
return A * x_3 - 5 * A * x_2 + 8 * A * x - 4 * A
return 0.0
i_start = int(np.floor(-2 / scale) + 1)
i_end = 2 - i_start
args = [scale * (i - ratio) for i in range(i_start, i_end)]
coeffs = [compute_coeff(x) for x in args]
return np.array(coeffs) / sum(coeffs)
def nearest_coeffs(
ratio: float | int | np.ndarray, mode: str = "round_prefer_floor"
) -> np.ndarray:
if isinstance(ratio, int) or ratio.is_integer():
return np.array([0, 1])
if mode == "round_prefer_floor":
return np.array([ratio <= 0.5, ratio > 0.5])
if mode == "round_prefer_ceil":
return np.array([ratio < 0.5, ratio >= 0.5])
if mode == "floor":
return np.array([1, 0])
if mode == "ceil":
return np.array([0, 1])
raise ValueError(f"Unexpected value {mode!r}.")
def _interpolate_1d_with_x(
data: np.ndarray,
scale_factor: float,
output_width_int: int,
x: float,
get_coeffs: Callable[[float, float], np.ndarray],
roi: np.ndarray | None = None,
extrapolation_value: float = 0.0,
coordinate_transformation_mode: str = "half_pixel",
exclude_outside: bool = False,
) -> np.ndarray:
input_width = len(data)
output_width = scale_factor * input_width
if coordinate_transformation_mode == "align_corners":
if output_width == 1:
x_ori = 0.0
else:
x_ori = x * (input_width - 1) / (output_width - 1)
elif coordinate_transformation_mode == "asymmetric":
x_ori = x / scale_factor
elif coordinate_transformation_mode == "tf_crop_and_resize":
if roi is None:
raise ValueError("roi cannot be None.")
if output_width == 1:
x_ori = (roi[1] - roi[0]) * (input_width - 1) / 2
else:
x_ori = x * (roi[1] - roi[0]) * (input_width - 1) / (output_width - 1)
x_ori += roi[0] * (input_width - 1)
if x_ori < 0 or x_ori > input_width - 1:
return np.array(extrapolation_value)
elif coordinate_transformation_mode == "pytorch_half_pixel":
if output_width == 1:
x_ori = -0.5
else:
x_ori = (x + 0.5) / scale_factor - 0.5
elif coordinate_transformation_mode == "half_pixel":
x_ori = (x + 0.5) / scale_factor - 0.5
elif coordinate_transformation_mode == "half_pixel_symmetric":
adjustment = output_width_int / output_width
center = input_width / 2
offset = center * (1 - adjustment)
x_ori = offset + (x + 0.5) / scale_factor - 0.5
else:
raise ValueError(
f"Invalid coordinate_transformation_mode: {coordinate_transformation_mode!r}."
)
x_ori_int = np.floor(x_ori).astype(int).item()
if x_ori.is_integer():
ratio = 1
else:
ratio = x_ori - x_ori_int
coeffs = get_coeffs(ratio, scale_factor)
n = len(coeffs)
idxes, points = _get_neighbor(x_ori, n, data)
if exclude_outside:
for i, idx in enumerate(idxes):
if idx < 0 or idx >= input_width:
coeffs[i] = 0
coeffs /= sum(coeffs)
return np.dot(coeffs, points).item()
def _interpolate_nd_with_x(
data: np.ndarray,
n: int,
scale_factors: list[float],
output_size: list[int],
x: list[float],
get_coeffs: Callable[[float, float], np.ndarray],
roi: np.ndarray | None = None,
exclude_outside: bool = False,
**kwargs: Any,
) -> np.ndarray:
if n == 1:
return _interpolate_1d_with_x(
data,
scale_factors[0],
output_size[0],
x[0],
get_coeffs,
roi=roi,
exclude_outside=exclude_outside,
**kwargs,
)
res1d = []
for i in range(data.shape[0]):
r = _interpolate_nd_with_x(
data[i],
n - 1,
scale_factors[1:],
output_size[1:],
x[1:],
get_coeffs,
roi=None if roi is None else np.concatenate([roi[1:n], roi[n + 1 :]]),
exclude_outside=exclude_outside,
**kwargs,
)
res1d.append(r)
return _interpolate_1d_with_x(
res1d,
scale_factors[0],
output_size[0],
x[0],
get_coeffs,
roi=None if roi is None else [roi[0], roi[n]],
exclude_outside=exclude_outside,
**kwargs,
)
def _get_all_coords(data: np.ndarray) -> np.ndarray:
return _cartesian(
[list(range(data.shape[i])) for i in range(len(data.shape))]
)
def interpolate_nd(
data: np.ndarray,
get_coeffs: Callable[[float, float], np.ndarray],
output_size: list[int] | None = None,
scale_factors: list[float] | None = None,
axes: list[int] | None = None,
roi: np.ndarray | None = None,
keep_aspect_ratio_policy: str | None = "stretch",
exclude_outside: bool = False,
**kwargs: Any,
) -> np.ndarray:
if output_size is None and scale_factors is None:
raise ValueError("output_size is None and scale_factors is None.")
r = len(data.shape)
if axes is not None:
if scale_factors is not None:
new_scale_factors = [1.0] * r
for i, d in enumerate(axes):
new_scale_factors[d] = scale_factors[i]
scale_factors = new_scale_factors
if output_size is not None:
new_output_size = [data.shape[i] for i in range(r)]
for i, d in enumerate(axes):
new_output_size[d] = output_size[i]
output_size = new_output_size
if roi is not None:
new_roi = ([0.0] * r) + ([1.0] * r)
naxes = len(axes)
for i, d in enumerate(axes):
new_roi[d] = roi[i]
new_roi[r + d] = roi[naxes + i]
roi = new_roi
else:
axes = list(range(r))
if output_size is not None:
scale_factors = [output_size[i] / data.shape[i] for i in range(r)]
if keep_aspect_ratio_policy != "stretch":
if keep_aspect_ratio_policy == "not_larger":
scale = np.array(scale_factors)[axes].min()
elif keep_aspect_ratio_policy == "not_smaller":
scale = np.array(scale_factors)[axes].max()
else:
raise ValueError(
f"Invalid keep_aspect_ratio_policy={keep_aspect_ratio_policy!r}"
)
scale_factors = [scale if i in axes else 1.0 for i in range(r)]
def round_half_up(x: float) -> int:
return int(x + 0.5)
output_size = [
round_half_up(scale * data.shape[i]) if i in axes else data.shape[i]
for i in range(r)
]
else:
output_size = (scale_factors * np.array(data.shape)).astype(int)
if scale_factors is None:
raise ValueError("scale_factors is None.")
if output_size is None:
raise ValueError("output_size is None.")
ret = np.zeros(output_size)
for x in _get_all_coords(ret):
ret[tuple(x)] = _interpolate_nd_with_x(
data,
len(data.shape),
scale_factors,
output_size,
x,
get_coeffs,
roi=roi,
exclude_outside=exclude_outside,
**kwargs,
)
return ret
def cubic_coeffs(
ratio: float, scale: float | None = None, A: float = -0.75
) -> np.ndarray:
del scale # Unused
coeffs = [
((A * (ratio + 1) - 5 * A) * (ratio + 1) + 8 * A) * (ratio + 1) - 4 * A,
((A + 2) * ratio - (A + 3)) * ratio * ratio + 1,
((A + 2) * (1 - ratio) - (A + 3)) * (1 - ratio) * (1 - ratio) + 1,
((A * ((1 - ratio) + 1) - 5 * A) * ((1 - ratio) + 1) + 8 * A)
* ((1 - ratio) + 1)
- 4 * A,
]
return np.array(coeffs)
class Resize(RunAll):
@staticmethod
def resize_upsample_scales_nearest() -> None:
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 3.0], dtype=np.float32)
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_nearest"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::HALF_PIXEL_SYMMETRIC),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_scales_nearest() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_scales_nearest"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_sizes_nearest() -> None:
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 7, 8], dtype=np.int64)
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes
).astype(np.float32)
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_sizes_nearest"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_sizes_nearest() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 1, 3], dtype=np.int64)
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes
).astype(np.float32)
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_sizes_nearest"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_scales_linear() -> None:
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
output = interpolate_nd(
data, lambda x, _: linear_coeffs(x, None), scale_factors=scales
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_linear"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_scales_linear_align_corners() -> None:
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x, None),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_linear_align_corners"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_scales_linear() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
output = interpolate_nd(
data, lambda x, _: linear_coeffs(x, None), scale_factors=scales
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_linear"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_scales_linear_align_corners() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x, None),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_scales_linear_align_corners"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_scales_cubic() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x, None), scale_factors=scales
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_cubic"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_scales_cubic_align_corners() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_cubic_align_corners"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_scales_cubic() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), scale_factors=scales
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_scales_cubic"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_scales_cubic_align_corners() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_scales_cubic_align_corners"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_sizes_cubic() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 9, 10], dtype=np.int64)
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), output_size=sizes
).astype(np.float32)
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_sizes_cubic"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_sizes_cubic() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
output = interpolate_nd(
data, lambda x, _: cubic_coeffs(x), output_size=sizes
).astype(np.float32)
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_sizes_cubic"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_scales_cubic_A_n0p5_exclude_outside() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x, A=-0.5),
scale_factors=scales,
exclude_outside=True,
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_cubic_A_n0p5_exclude_outside"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(FixedTrait::<FP16x16>::new(32768, true)),"
func_sig += "Option::Some(true),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_scales_cubic_A_n0p5_exclude_outside() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.8, 0.8], dtype=np.float32)
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x, A=-0.5),
scale_factors=scales,
exclude_outside=True,
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_scales_cubic_A_n0p5_exclude_outside"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(FixedTrait::<FP16x16>::new(32768, true)),"
func_sig += "Option::Some(true),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_scales_cubic_asymmetric() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
output = interpolate_nd(
data,
lambda x, _: cubic_coeffs(x, A=-0.75),
scale_factors=scales,
coordinate_transformation_mode="asymmetric",
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_cubic_asymmetric"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::ASYMMETRIC),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_tf_crop_and_resize() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
roi = np.array([0, 0, 0.4, 0.6, 1, 1, 0.6, 0.8], dtype=np.float32)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
coordinate_transformation_mode="tf_crop_and_resize",
).astype(np.float32)
x = [data, sizes, roi]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.FP16x16, x[2].shape, to_fp(x[2].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_tf_crop_and_resize"
func_sig = "data.resize("
func_sig += "roi,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_tf_crop_and_resize_extrapolation_value() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
roi = np.array([0, 0, 0.4, 0.6, 1, 1, 1.2, 1.7], dtype=np.float32)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
coordinate_transformation_mode="tf_crop_and_resize",
extrapolation_value=10.0,
).astype(np.float32)
x = [data, sizes, roi]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.FP16x16, x[2].shape, to_fp(x[2].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_tf_crop_and_resize_extrapolation_value"
func_sig = "data.resize("
func_sig += "roi,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(FixedTrait::<FP16x16>::new(655360, false)),"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_downsample_sizes_linear_pytorch_half_pixel() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 1], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
coordinate_transformation_mode="pytorch_half_pixel",
).astype(np.float32)
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_sizes_linear_pytorch_half_pixel"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::PYTORCH_HALF_PIXEL),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_sizes_nearest_floor_align_corners() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 8, 8], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x, mode="floor"),
output_size=sizes,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_sizes_nearest_floor_align_corners"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::ALIGN_CORNERS),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::Some(NEAREST_MODE::FLOOR),)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 8, 8], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x, mode="round_prefer_ceil"),
output_size=sizes,
coordinate_transformation_mode="asymmetric",
).astype(np.float32)
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_sizes_nearest_round_prefer_ceil_asymmetric"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::ASYMMETRIC),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::Some(NEAREST_MODE::ROUND_PREFER_CEIL),)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_sizes_nearest_ceil_half_pixel() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 8, 8], dtype=np.int64)
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x, mode="ceil"), output_size=sizes
).astype(np.float32)
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_sizes_nearest_ceil_half_pixel"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::HALF_PIXEL),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::Some(NEAREST_MODE::CEIL),)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_scales_linear_antialias() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
output = interpolate_nd(
data, linear_coeffs_antialias, scale_factors=scales
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_scales_linear_antialias"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::Some(1),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_sizes_linear_antialias() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
output = interpolate_nd(
data, linear_coeffs_antialias, output_size=sizes
).astype(np.float32)
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_sizes_linear_pytorch_half_pixel"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::Some(1),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_scales_cubic_antialias() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 0.6, 0.6], dtype=np.float32)
output = interpolate_nd(
data, cubic_coeffs_antialias, scale_factors=scales
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_scales_cubic_antialias"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::Some(1),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_sizes_cubic_antialias() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 3], dtype=np.int64)
output = interpolate_nd(data, cubic_coeffs_antialias, output_size=sizes).astype(
np.float32
)
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_sizes_cubic_antialias"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::Some(1),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_scales_nearest_axes_2_3() -> None:
axes = np.array([2, 3], dtype=np.int64)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
scales = np.array([2.0, 3.0], dtype=np.float32)
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes
).astype(np.float32)
x = [data, scales, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.FP16x16, x[1].shape, to_fp(x[1].flatten(), FixedImpl.FP16x16))
x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_nearest_axes_2_3"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_upsample_scales_nearest_axes_3_2() -> None:
axes = np.array([3, 2], dtype=np.int64)
data = np.array([[[[1, 2],[3, 4],]]],dtype=np.float32,)
scales = np.array([3.0, 2.0], dtype=np.float32)
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), scale_factors=scales, axes=axes
).astype(np.float32)
x = [data, scales, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.FP16x16, x[1].shape, to_fp(x[1].flatten(), FixedImpl.FP16x16))
x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_nearest_axes_3_2"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_upsample_sizes_nearest_axes_2_3() -> None:
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([7, 8], dtype=np.int64)
axes = np.array([2, 3], dtype=np.int64)
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes
).astype(np.float32)
x = [data, sizes, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_sizes_nearest_axes_2_3"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_upsample_sizes_nearest_axes_3_2() -> None:
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([8, 7], dtype=np.int64)
axes = np.array([3, 2], dtype=np.int64)
output = interpolate_nd(
data, lambda x, _: nearest_coeffs(x), output_size=sizes, axes=axes
).astype(np.float32)
x = [data, sizes, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_sizes_nearest_axes_3_2"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_tf_crop_and_resize_axes_2_3() -> None:
axes = np.array([2, 3], dtype=np.int64)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
roi = np.array([0.4, 0.6, 0.6, 0.8], dtype=np.float32)
sizes = np.array([3, 3], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
axes=axes,
coordinate_transformation_mode="tf_crop_and_resize",
).astype(np.float32)
x = [data, sizes, roi, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.FP16x16, x[2].shape, to_fp(x[2].flatten(), FixedImpl.FP16x16))
x[3] = Tensor(Dtype.U32, x[3].shape, x[3].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_tf_crop_and_resize_axes_2_3"
func_sig = "data.resize("
func_sig += "roi,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2], x[3]], y, func_sig, name)
@staticmethod
def resize_tf_crop_and_resize_axes_3_2() -> None:
axes = np.array([3, 2], dtype=np.int64)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
roi = np.array([0.6, 0.4, 0.8, 0.6], dtype=np.float32)
sizes = np.array([3, 3], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
output_size=sizes,
roi=roi,
axes=axes,
coordinate_transformation_mode="tf_crop_and_resize",
).astype(np.float32)
x = [data, sizes, roi, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.FP16x16, x[2].shape, to_fp(x[2].flatten(), FixedImpl.FP16x16))
x[3] = Tensor(Dtype.U32, x[3].shape, x[3].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_tf_crop_and_resize_axes_3_2"
func_sig = "data.resize("
func_sig += "roi,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::Some(TRANSFORMATION_MODE::TF_CROP_AND_RESIZE),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2], x[3]], y, func_sig, name)
@staticmethod
def resize_upsample_sizes_nearest_not_larger() -> None:
keep_aspect_ratio_policy = "not_larger"
axes = np.array([2, 3], dtype=np.int64)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([7, 8], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
x = [data, sizes, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_sizes_nearest_not_larger"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_LARGER),"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_upsample_sizes_nearest_not_smaller() -> None:
keep_aspect_ratio_policy = "not_smaller"
axes = np.array([2, 3], dtype=np.int64)
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
)
sizes = np.array([7, 8], dtype=np.int64) # Results in 8x8
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
x = [data, sizes, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_sizes_nearest_not_smaller"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_SMALLER),"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_downsample_sizes_nearest_not_larger() -> None:
keep_aspect_ratio_policy = "not_larger"
axes = np.array([2, 3], dtype=np.int64)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 3], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
x = [data, sizes, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_sizes_nearest_not_larger"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_LARGER),"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_downsample_sizes_nearest_not_smaller() -> None:
keep_aspect_ratio_policy = "not_smaller"
axes = np.array([2, 3], dtype=np.int64)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 3], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
).astype(np.float32)
x = [data, sizes, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_sizes_nearest_not_smaller"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_SMALLER),"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_downsample_scales_linear_half_pixel_symmetric() -> None:
data = np.array([[[[1, 2, 3, 4]]]], dtype=np.float32)
scales = np.array([1.0, 1.0, 1.0, 0.6], dtype=np.float32)
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="half_pixel_symmetric",
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_scales_linear_half_pixel_symmetric"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::HALF_PIXEL_SYMMETRIC),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_scales_linear_half_pixel_symmetric() -> None:
data = np.array([[[[1, 2], [3, 4]]]], dtype=np.float32)
scales = np.array([1.0, 1.0, 2.3, 2.94], dtype=np.float32)
output = interpolate_nd(
data,
lambda x, _: linear_coeffs(x),
scale_factors=scales,
coordinate_transformation_mode="half_pixel_symmetric",
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_linear_half_pixel_symmetric"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::HALF_PIXEL_SYMMETRIC),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/reverse_sequence.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Reverse_sequence(RunAll):
@staticmethod
def Reverse_sequence_u32():
def reverse_sequence_u32_4x4_batch():
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=np.uint32).reshape((4, 4))
y = np.array([0, 1, 2, 3, 5, 4, 6, 7, 10, 9, 8, 11, 15, 14, 13, 12], dtype=np.uint32).reshape((4, 4))
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reverse_sequence_u32_4x4_batch"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![1,2,3,4].span()), Option::Some(0), Option::Some(1))",
name
)
def reverse_sequence_u32_4x4_time():
x = np.array([0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15], dtype=np.uint32).reshape((4, 4))
y = np.array([3, 6, 9, 12, 2, 5, 8, 13, 1, 4, 10, 14, 0, 7, 11, 15], dtype=np.uint32).reshape((4, 4))
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reverse_sequence_u32_4x4_time"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![4,3,2,1].span()), Option::Some(1), Option::Some(0))",
name
)
def reverse_sequence_u32_3x3_batch():
x = np.array([0,1,2,3,4,5,6,7,8], dtype=np.uint32).reshape(3,3)
y = np.array([2,1,0,3,4,5,7,6,8], dtype=np.uint32).reshape(3,3)
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reverse_sequence_u32_3x3_batch"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![3].span(), array![3,1,2].span()), Option::Some(0), Option::Some(1))",
name
)
def reverse_sequence_u32_3x3_time():
x = np.array([0,1,2,3,4,5,6,7,8], dtype=np.uint32).reshape(3,3)
y = np.array([0,7,8,3,4,5,6,1,2], dtype=np.uint32).reshape(3,3)
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reverse_sequence_u32_3x3_time"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![3].span(), array![1,3,3].span()), Option::Some(1), Option::Some(0))",
name
)
reverse_sequence_u32_4x4_batch()
reverse_sequence_u32_4x4_time()
reverse_sequence_u32_3x3_batch()
reverse_sequence_u32_3x3_time()
@staticmethod
def Reverse_sequence_i32():
def reverse_sequence_i32_batch():
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=np.int32).reshape((4, 4))
y = np.array([0, 1, 2, 3, 5, 4, 6, 7, 10, 9, 8, 11, 15, 14, 13, 12], dtype=np.int32).reshape((4, 4))
_x = Tensor(Dtype.I32, x.shape, x.flatten())
_y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reverse_sequence_i32_batch_equal_parts"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![1,2,3,4].span()), Option::Some(0), Option::Some(1))",
name
)
def reverse_sequence_i32_time():
x = np.array([0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15], dtype=np.int32).reshape((4, 4))
y = np.array([3, 6, 9, 12, 2, 5, 8, 13, 1, 4, 10, 14, 0, 7, 11, 15], dtype=np.int32).reshape((4, 4))
_x = Tensor(Dtype.I32, x.shape, x.flatten())
_y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reverse_sequence_i32_time_equal_parts"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![4,3,2,1].span()), Option::Some(1), Option::Some(0))",
name
)
reverse_sequence_i32_batch()
reverse_sequence_i32_time()
@staticmethod
def Reverse_sequence_i8():
def reverse_sequence_batch():
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=np.int8).reshape((4, 4))
y = np.array([0, 1, 2, 3, 5, 4, 6, 7, 10, 9, 8, 11, 15, 14, 13, 12], dtype=np.int8).reshape((4, 4))
_x = Tensor(Dtype.I8, x.shape, x.flatten())
_y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "reverse_sequence_i8_batch_equal_parts"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![1,2,3,4].span()), Option::Some(0), Option::Some(1))",
name
)
def reverse_sequence_time():
x = np.array([0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15], dtype=np.uint32).reshape((4, 4))
y = np.array([3, 6, 9, 12, 2, 5, 8, 13, 1, 4, 10, 14, 0, 7, 11, 15], dtype=np.uint32).reshape((4, 4))
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reverse_sequence_i8_time_equal_parts"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![4,3,2,1].span()), Option::Some(1), Option::Some(0))",
name
)
reverse_sequence_batch()
reverse_sequence_time()
def Reverse_sequence_fp16x16():
def reverse_sequence_batch():
x = to_fp(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=np.int64).reshape(4, 4), FixedImpl.FP16x16)
y = to_fp(np.array([0, 1, 2, 3, 5, 4, 6, 7, 10, 9, 8, 11, 15, 14, 13, 12], dtype=np.int64).reshape(4, 4), FixedImpl.FP16x16)
_x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
_y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reverse_sequence_fp16x16_batch_equal_parts"
make_test(
[_x],
_y, "input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![1,2,3,4].span()), Option::Some(0), Option::Some(1))",
name
)
def reverse_sequence_time():
x = to_fp(np.array([0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15], dtype=np.int64).reshape(4, 4), FixedImpl.FP16x16)
y = to_fp(np.array([3, 6, 9, 12, 2, 5, 8, 13, 1, 4, 10, 14, 0, 7, 11, 15], dtype=np.int64).reshape(4, 4), FixedImpl.FP16x16)
_x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
_y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reverse_sequence_fp16x16_time_equal_parts"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![4,3,2,1].span()), Option::Some(1), Option::Some(0))",
name
)
reverse_sequence_batch()
reverse_sequence_time()
def reverse_sequence_different_dimensions():
def reverse_sequence_different_dimensions_4_5():
x = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], dtype=np.uint32).reshape(4,5)
y = np.array([5,4,3,2,1,9,8,7,6,10,13,12,11,14,15,17,16,18,19,20], dtype=np.uint32).reshape(4,5)
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reverse_sequence_different_dimensions_4_5"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![5,4,3,2].span()), Option::Some(0), Option::Some(1))",
name
)
def reverse_sequence_different_dimensions_2_4():
x = np.array([1,2,3,4,5,6,7,8], dtype=np.uint32).reshape(2,4)
y = np.array([5,6,7,8,1,2,3,4], dtype=np.uint32).reshape(2,4)
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reverse_sequence_different_dimensions_2_4"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![2,2,2,2].span()), Option::Some(1), Option::Some(0))",
name
)
def reverse_sequence_different_dimensions_1_6():
x = np.array([0,1,2,3,4,5], dtype=np.uint32).reshape(1,6)
y = np.array([4,3,2,1,0,5], dtype=np.uint32).reshape(1,6)
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reverse_sequence_different_dimensions_1_6"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![1].span(), array![5].span()), Option::Some(0), Option::Some(1))",
name
)
def reverse_sequence_different_dimensions_3x9_batch():
x = np.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26], dtype=np.uint32).reshape(3,9)
y = np.array([6,5,4,3,2,1,0,7,8,16,15,14,13,12,11,10,9,17,26,25,24,23,22,21,20,19,18], dtype=np.uint32).reshape(3,9)
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reverse_sequence_different_dimensions_3x9_batch"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![3].span(), array![7,8,9].span()), Option::Some(0), Option::Some(1))",
name
)
def reverse_sequence_different_dimensions_3x9_time():
x = np.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26], dtype=np.uint32).reshape(3,9)
y = np.array([18,10,20,12,22,14,24,16,8,9,1,11,3,13,5,15,7,17,0,19,2,21,4,23,6,25,26], dtype=np.uint32).reshape(3,9)
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reverse_sequence_different_dimensions_3x9_time"
make_test(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![9].span(), array![3,2,3,2,3,2,3,2,1].span()), Option::Some(1), Option::Some(0))",
name
)
reverse_sequence_different_dimensions_4_5()
reverse_sequence_different_dimensions_2_4()
reverse_sequence_different_dimensions_1_6()
reverse_sequence_different_dimensions_3x9_batch()
reverse_sequence_different_dimensions_3x9_time()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/round.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Round(RunAll):
@staticmethod
def round_fp8x23():
x = np.array([0.1, 0.5, 0.9, 1.2, 1.5, 1.8, 2.3, 2.5, 2.7, -1.1, -1.5, -1.9, -2.2, -2.5, -2.8]).astype(np.float64)
y = np.array([0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, -1.0, -2.0, -2.0, -2.0, -3.0, -3.0]).astype(np.float64)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "round_fp8x23"
make_test([x], y, "input_0.round()", name)
@staticmethod
def round_fp16x16():
x = np.array([0.1, 0.5, 0.9, 1.2, 1.5, 1.8, 2.3, 2.5, 2.7, -1.1, -1.5, -1.9, -2.2, -2.5, -2.8]).astype(np.float64)
y = np.array([0.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, -1.0, -2.0, -2.0, -2.0, -3.0, -3.0]).astype(np.float64)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "round_fp16x16"
make_test([x], y, "input_0.round()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/running.py
|
import os
import glob
# Directory path where Python files/modules are located
directory_path = 'nodegen/node/'
# Get all files in the directory
all_files = os.listdir(directory_path)
# Filter Python files using glob and '*.py' pattern
python_files = [file[:-3] for file in all_files if file.endswith('.py')]
fixed = [
'abs',
'argmax',
'argmin',
'concat',
'cumsum',
'div',
'equal',
'less_equal',
'greater',
'linear',
'matmul',
'mul',
'or',
'reduce_sum',
'sub',
'transpose',
'xor',
'less',
'greater_equal',
'slice',
'gather',
'nonzero',
'squeeze',
'unsqueeze',
'sign',
'clip',
'__init__',
'running'
]
for node in python_files:
if node not in fixed:
current_dir = os.getcwd()
command = f'python nodegen/node/__init__.py {node}'
os.system(command)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/scatter.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
# The below ScatterElements' numpy implementation is from https://stackoverflow.com/a/46204790/11767360
def scatter_elements(data, indices, updates, axis=0, reduction="none"): # type: ignore
if axis < 0:
axis = data.ndim + axis
idx_xsection_shape = indices.shape[:axis] + indices.shape[axis + 1 :]
def make_slice(arr, axis, i): # type: ignore
slc = [slice(None)] * arr.ndim
slc[axis] = i
return slc
def unpack(packed): # type: ignore
unpacked = packed[0]
for i in range(1, len(packed)):
unpacked = unpacked, packed[i]
return unpacked
def make_indices_for_duplicate(idx): # type: ignore
final_idx = []
for i in range(len(idx[0])):
final_idx.append(tuple(idx_element[i] for idx_element in idx))
return list(final_idx)
# We use indices and axis parameters to create idx
# idx is in a form that can be used as a NumPy advanced indices for scattering of updates param. in data
idx = [
[
unpack(np.indices(idx_xsection_shape).reshape(indices.ndim - 1, -1)),
indices[tuple(make_slice(indices, axis, i))].reshape(1, -1)[0],
]
for i in range(indices.shape[axis])
]
idx = list(np.concatenate(idx, axis=1))
idx.insert(axis, idx.pop())
# updates_idx is a NumPy advanced indices for indexing of elements in the updates
updates_idx = list(idx)
updates_idx.pop(axis)
updates_idx.insert(
axis, np.repeat(np.arange(indices.shape[axis]), np.prod(idx_xsection_shape))
)
scattered = np.copy(data)
if reduction == "none":
scattered[tuple(idx)] = updates[tuple(updates_idx)]
else:
idx, updates_idx = make_indices_for_duplicate(idx), make_indices_for_duplicate(
updates_idx
)
for iter, idx_set in enumerate(idx):
if reduction == "add":
scattered[idx_set] += updates[updates_idx[iter]]
elif reduction == "mul":
scattered[idx_set] *= updates[updates_idx[iter]]
elif reduction == "max":
scattered[idx_set] = np.maximum(
scattered[idx_set], updates[updates_idx[iter]]
)
elif reduction == "min":
scattered[idx_set] = np.minimum(
scattered[idx_set], updates[updates_idx[iter]]
)
return scattered
class Scatter(RunAll):
@staticmethod
def scatter_fp16x16():
def scatter():
def default():
x1 = np.zeros((3, 3)).astype(np.int64)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int64)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 0, 'none')
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.FP16x16, x2.shape, to_fp(x2.flatten(), FixedImpl.FP16x16))
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "scatter_fp16x16_3d_default"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(0), reduction:Option::Some('none'))",
name= name)
def axis_1():
x1 = np.zeros((3, 3)).astype(np.int64)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int64)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 1, 'none')
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.FP16x16, x2.shape, to_fp(x2.flatten(), FixedImpl.FP16x16))
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "scatter_fp16x16_3d_axis1"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(1), reduction:Option::Some('none'))",
name= name)
def axis_1_add():
x1 = np.zeros((3, 3)).astype(np.int64)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int64)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 1, 'add')
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.FP16x16, x2.shape, to_fp(x2.flatten(), FixedImpl.FP16x16))
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "scatter_fp16x16_3d_axis1_add"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(1), reduction:Option::Some('add'))",
name= name)
default()
axis_1()
axis_1_add()
scatter()
@staticmethod
def scatter_fp8x23():
def scatter():
def default():
x1 = np.zeros((3, 3)).astype(np.int64)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int64)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 0, 'none')
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.FP8x23, x2.shape, to_fp(x2.flatten(), FixedImpl.FP8x23))
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "scatter_fp8x23_default"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(0), reduction:Option::Some('none'))",
name= name)
def axis1():
x1 = np.zeros((3, 3)).astype(np.int64)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int64)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 1, 'none')
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.FP8x23, x2.shape, to_fp(x2.flatten(), FixedImpl.FP8x23))
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "scatter_fp8x23_axis1"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(1), reduction:Option::Some('none'))",
name= name)
def axis1_mul():
x1 = np.zeros((3, 3)).astype(np.int64)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int64)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 0, 'mul')
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.FP8x23, x2.shape, to_fp(x2.flatten(), FixedImpl.FP8x23))
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedImpl.FP8x23))
name = "scatter_fp8x23_mul"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(0), reduction:Option::Some('mul'))",
name= name)
default()
axis1()
axis1_mul()
scatter()
@staticmethod
def scatter_i8():
def scatter_3D():
def default():
x1 = np.zeros((3, 3)).astype(np.int8)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int8)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 0, 'none')
x1 = Tensor(Dtype.I8, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I8, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "scatter_i8_default"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(0), reduction:Option::Some('none'))",
name= name)
def axis1():
x1 = np.zeros((3, 3)).astype(np.int8)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int8)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 1, 'none')
x1 = Tensor(Dtype.I8, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I8, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "scatter_i8_axis1"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(1), reduction:Option::Some('none'))",
name= name)
def axis1_max():
x1 = np.zeros((3, 3)).astype(np.int8)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int8)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 1, 'max')
x1 = Tensor(Dtype.I8, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I8, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "scatter_i8_axis1_max"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(1), reduction:Option::Some('max'))",
name= name)
default()
axis1()
axis1_max()
scatter_3D()
@staticmethod
def scatter_i32():
def scatter_3D():
def default():
x1 = np.zeros((3, 3)).astype(np.int32)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int32)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 0, 'none')
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "scatter_i8_default"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(0), reduction:Option::Some('none'))",
name= name)
def axis1():
x1 = np.zeros((3, 3)).astype(np.int32)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int32)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 1, 'none')
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "scatter_i8_axis1"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(1), reduction:Option::Some('none'))",
name= name)
def axis_min():
x1 = np.zeros((3, 3)).astype(np.int32)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int32)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 1, 'min')
x1 = Tensor(Dtype.I32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.I32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "scatter_i8_default"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(1), reduction:Option::Some('min'))",
name= name)
default()
axis1()
axis_min()
scatter_3D()
@staticmethod
def scatter_u32():
def scatter_3D():
def default():
x1 = np.zeros((3, 3)).astype(np.uint32)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.uint32)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 0, 'none')
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "scatter_u32_default"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(0), reduction:Option::Some('none'))",
name= name)
def axis1():
x1 = np.zeros((3, 3)).astype(np.uint32)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.uint32)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 1, 'none')
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "scatter_u32_axis1"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(1), reduction:Option::Some('none'))",
name= name)
def axis_add():
x1 = np.zeros((3, 3)).astype(np.uint32)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.uint32)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 0, 'add')
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "scatter_u32_add"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(0), reduction:Option::Some('add'))",
name= name)
default()
axis1()
axis_add()
scatter_3D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/scatter_nd.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
def scatter_nd_impl(data, indices, updates, reduction="none"): # type: ignore
# Check tensor shapes
assert indices.shape[-1] <= len(data.shape)
assert updates.shape == indices.shape[:-1] + data.shape[indices.shape[-1] :]
# Compute output
output = np.copy(data)
for i in np.ndindex(indices.shape[:-1]):
# NOTE: The order of iteration in this loop is not specified.
if reduction == "add":
output[tuple(indices[i])] += updates[i]
elif reduction == "mul":
output[tuple(indices[i])] *= updates[i]
elif reduction == "max":
output[tuple(indices[i])] = np.maximum(output[indices[i]], updates[i])
elif reduction == "min":
output[tuple(indices[i])] = np.minimum(output[indices[i]], updates[i])
else:
output[tuple(indices[i])] = updates[i]
return output
data = np.array(
[
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
[[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
],
dtype=np.float32,
)
indices = np.array([[0], [2]], dtype=np.int64)
updates = np.array(
[
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
],
dtype=np.float32,
)
class Scatter_nd(RunAll):
@staticmethod
def scatter_nd_fp16x16():
def scatter_nd_3D():
def default():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='none')
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP16x16, x3.shape, to_fp(x3.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "scatter_nd_fp16x16_3d_default"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::None(()))",
name= name)
def add():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='add')
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP16x16, x3.shape, to_fp(x3.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "scatter_nd_fp16x16_3d_add"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('add'))",
name= name)
def mul():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='mul')
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP16x16, x3.shape, to_fp(x3.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "scatter_nd_fp16x16_3d_mul"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('mul'))",
name= name)
def max():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='max')
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP16x16, x3.shape, to_fp(x3.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "scatter_nd_fp16x16_3d_max"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('max'))",
name= name)
def min():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='min')
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP16x16, x3.shape, to_fp(x3.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "scatter_nd_fp16x16_3d_min"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('min'))",
name= name)
default()
add()
mul()
max()
min()
scatter_nd_3D()
@staticmethod
def scatter_nd_fp8x23():
def scatter_nd_3D():
def default():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='none')
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP8x23, x3.shape, to_fp(x3.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "scatter_nd_fp8x23_3d_default"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::None(()))",
name= name)
def add():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='add')
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP8x23, x3.shape, to_fp(x3.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "scatter_nd_fp8x23_3d_add"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('add'))",
name= name)
def mul():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='mul')
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP8x23, x3.shape, to_fp(x3.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "scatter_nd_fp8x23_3d_mul"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('mul'))",
name= name)
def max():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='max')
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP8x23, x3.shape, to_fp(x3.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "scatter_nd_fp8x23_3d_max"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('max'))",
name= name)
def min():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='min')
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP8x23, x3.shape, to_fp(x3.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "scatter_nd_fp8x23_3d_min"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('min'))",
name= name)
default()
add()
mul()
max()
min()
scatter_nd_3D()
@staticmethod
def scatter_nd_u32():
def scatter_nd_3D():
def default():
x1 = np.arange(0,12).reshape((4,3)).astype(np.int32)
x2 = np.array([[0],[1]]).astype(np.uint32)
x3 = np.random.randint(low = 0,high=100, size=(2,3)).astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='none')
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "scatter_nd_u32_default"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::None(()))",
name= name)
def add():
x1 = np.arange(0,12).reshape((4,3)).astype(np.int32)
x2 = np.array([[1],[0]]).astype(np.uint32)
x3 = np.random.randint(low = 0,high=100, size=(2,3)).astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='add')
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "scatter_nd_u32_add"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('add'))",
name= name)
def mul():
x1 = np.arange(0,12).reshape((4,3)).astype(np.int32)
x2 =np.array([[0],[1]]).astype(np.uint32)
x3 = np.random.randint(low = 0,high=100, size=(2,3)).astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='mul')
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "scatter_nd_u32_mul"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('mul'))",
name= name)
def max():
x1 = np.arange(0,12).reshape((4,3)).astype(np.int32)
x2 =np.array([[0],[1]]).astype(np.uint32)
x3 = np.random.randint(low = 0,high=100, size=(2,3)).astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='max')
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "scatter_nd_u32_max"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('max'))",
name= name)
def min():
x1 = np.arange(0,12).reshape((4,3)).astype(np.int32)
x2 = np.array([[0],[1]]).astype(np.uint32)
x3 = np.random.randint(low = 0,high=100, size=(2,3)).astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='min')
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "scatter_nd_u32_min"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('min'))",
name= name)
default()
add()
mul()
max()
min()
scatter_nd_3D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/sequence_at.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
scalar = lambda x: Tensor(Dtype.I32, (), np.array([x]).astype(np.int32).flatten())
class Sequence_at(RunAll):
@staticmethod
def sequence_at_u32():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(2)
name = "sequence_at_u32_positive"
make_test([sequence, position], sequence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(-2)
name = "sequence_at_u32_negative"
make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
positive_position()
negative_position()
@staticmethod
def sequence_at_i32():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(2)
name = "sequence_at_i32_positive"
make_test([sequence, position], sequence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(-2)
name = "sequence_at_i32_negative"
make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
positive_position()
negative_position()
@staticmethod
def sequence_at_i8():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(2)
name = "sequence_at_i8_positive"
make_test([sequence, position], sequence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(-2)
name = "sequence_at_i8_negative"
make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
positive_position()
negative_position()
@staticmethod
def sequence_at_fp8x23():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
position = scalar(2)
name = "sequence_at_fp8x23_positive"
make_test([sequence, position], sequence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
position = scalar(-2)
name = "sequence_at_fp8x23_negative"
make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
positive_position()
negative_position()
@staticmethod
def sequence_at_fp16x16():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
position = scalar(2)
name = "sequence_at_fp16x16_positive"
make_test([sequence, position], sequence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
position = scalar(-2)
name = "sequence_at_fp16x16_negative"
make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
positive_position()
negative_position()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/sequence_construct.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
class Sequence_construct(RunAll):
@staticmethod
def sequence_construct_u32():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
name = "sequence_construct_u32"
make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
@staticmethod
def sequence_construct_i32():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
name = "sequence_construct_i32"
make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
@staticmethod
def sequence_construct_i8():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
name = "sequence_construct_i8"
make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
@staticmethod
def sequence_construct_fp8x23():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
name = "sequence_construct_fp8x23"
make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
@staticmethod
def sequence_construct_fp16x16():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
name = "sequence_construct_fp16x16"
make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/sequence_empty.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, Dtype, Tensor, Trait
class Sequence_empty(RunAll):
@staticmethod
def sequence_empty_u32():
def default():
shape=(0,)
x = np.zeros(shape, dtype=np.uint32)
t = Tensor(Dtype.U32, shape, x.flatten())
make_test(
inputs=[],
output=[t],
func_sig="SequenceTrait::sequence_empty()",
name="sequence_empty_u32",
trait=Trait.SEQUENCE
)
default()
@staticmethod
def sequence_empty_i32():
def default():
shape=(0,)
x = np.zeros(shape, dtype=np.int32)
t = Tensor(Dtype.I32, shape, x.flatten())
make_test(
inputs=[],
output=[t],
func_sig="SequenceTrait::sequence_empty()",
name="sequence_empty_i32",
trait=Trait.SEQUENCE
)
default()
@staticmethod
def sequence_empty_i8():
def default():
shape=(0,)
x = np.zeros(shape, dtype=np.int8)
t = Tensor(Dtype.I8, shape, x.flatten())
make_test(
inputs=[],
output=[t],
func_sig="SequenceTrait::sequence_empty()",
name="sequence_empty_i8",
trait=Trait.SEQUENCE
)
default()
@staticmethod
def sequence_empty_fp8x23():
def default():
shape=(0,)
x = np.zeros(shape, dtype=np.float64)
t = Tensor(Dtype.FP8x23, shape, x.flatten())
make_test(
inputs=[],
output=[t],
func_sig="SequenceTrait::sequence_empty()",
name="sequence_empty_fp8x23",
trait=Trait.SEQUENCE
)
default()
@staticmethod
def sequence_empty_fp16x16():
def default():
shape=(0,)
x = np.zeros(shape, dtype=np.float64)
t = Tensor(Dtype.FP16x16, shape, x.flatten())
make_test(
inputs=[],
output=[t],
func_sig="SequenceTrait::sequence_empty()",
name="sequence_empty_fp16x16",
trait=Trait.SEQUENCE
)
default()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/sequence_erase.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
scalar = lambda x: Tensor(Dtype.I32, (), np.array([x]).astype(np.int32).flatten())
class Sequence_erase(RunAll):
@staticmethod
def sequence_erase_u32():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(2)
output_sequence = sequence.copy()
output_sequence.pop(2)
name = "sequence_erase_u32_positive"
make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(-2)
output_sequence = sequence.copy()
output_sequence.pop(-2)
name = "sequence_erase_u32_negative"
make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
def empty_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
output_sequence = sequence.copy()
output_sequence.pop(-1)
name = "sequence_erase_u32_empty"
make_test([sequence], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::None(()))", name, Trait.SEQUENCE)
positive_position()
negative_position()
empty_position()
@staticmethod
def sequence_erase_i32():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(2)
output_sequence = sequence.copy()
output_sequence.pop(2)
name = "sequence_erase_i32_positive"
make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(-2)
output_sequence = sequence.copy()
output_sequence.pop(-2)
name = "sequence_erase_i32_negative"
make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
def empty_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
output_sequence = sequence.copy()
output_sequence.pop(-1)
name = "sequence_erase_i32_empty"
make_test([sequence], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::None(()))", name, Trait.SEQUENCE)
positive_position()
negative_position()
empty_position()
@staticmethod
def sequence_erase_i8():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(2)
output_sequence = sequence.copy()
output_sequence.pop(2)
name = "sequence_erase_i8_positive"
make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
position = scalar(-2)
output_sequence = sequence.copy()
output_sequence.pop(-2)
name = "sequence_erase_i8_negative"
make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
def empty_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
output_sequence = sequence.copy()
output_sequence.pop(-1)
name = "sequence_erase_i8_empty"
make_test([sequence], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::None(()))", name, Trait.SEQUENCE)
positive_position()
negative_position()
empty_position()
@staticmethod
def sequence_erase_fp8x23():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
position = scalar(2)
output_sequence = sequence.copy()
output_sequence.pop(2)
name = "sequence_erase_fp8x23_positive"
make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
position = scalar(-2)
output_sequence = sequence.copy()
output_sequence.pop(-2)
name = "sequence_erase_fp8x23_negative"
make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
def empty_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
output_sequence = sequence.copy()
output_sequence.pop(-1)
name = "sequence_erase_fp8x23_empty"
make_test([sequence], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::None(()))", name, Trait.SEQUENCE)
positive_position()
negative_position()
empty_position()
@staticmethod
def sequence_erase_fp16x16():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
position = scalar(2)
output_sequence = sequence.copy()
output_sequence.pop(2)
name = "sequence_erase_fp16x16_positive"
make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
position = scalar(-2)
output_sequence = sequence.copy()
output_sequence.pop(-2)
name = "sequence_erase_fp16x16_negative"
make_test([sequence, position], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::Some(input_1))", name, Trait.SEQUENCE)
def empty_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
output_sequence = sequence.copy()
output_sequence.pop(-1)
name = "sequence_erase_fp16x16_empty"
make_test([sequence], output_sequence, "SequenceTrait::sequence_erase(input_0, Option::None(()))", name, Trait.SEQUENCE)
positive_position()
negative_position()
empty_position()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/sequence_insert.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
scalar = lambda x: Tensor(Dtype.I32, (), np.array([x]).astype(np.int32).flatten())
class Sequence_insert(RunAll):
@staticmethod
def sequence_insert_u32():
def default():
sequence = []
tensor_cnt = 3
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
val = np.random.randint(0, 6, shape).astype(np.uint32)
t = Tensor(Dtype.U32, val.shape, val.flatten())
sequence.append(t)
val = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, val.shape, val.flatten())
position = np.random.randint(-2, 2)
expected_sequence = sequence.copy()
expected_sequence.insert(position, tensor)
name = "sequence_insert_u32"
make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name, Trait.SEQUENCE)
default()
@staticmethod
def sequence_insert_i32():
def default():
sequence = []
tensor_cnt = 3
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
val = np.random.randint(0, 6, shape).astype(np.int32)
t = Tensor(Dtype.I32, val.shape, val.flatten())
sequence.append(t)
val = np.random.randint(0, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, val.shape, val.flatten())
position = np.random.randint(-2, 2)
expected_sequence = sequence.copy()
expected_sequence.insert(position, tensor)
name = "sequence_insert_i32"
make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name, Trait.SEQUENCE)
default()
@staticmethod
def sequence_insert_i8():
def default():
sequence = []
tensor_cnt = 3
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
val = np.random.randint(0, 6, shape).astype(np.int8)
t = Tensor(Dtype.I8, val.shape, val.flatten())
sequence.append(t)
val = np.random.randint(0, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, val.shape, val.flatten())
position = np.random.randint(-2, 2)
expected_sequence = sequence.copy()
expected_sequence.insert(position, tensor)
name = "sequence_insert_i8"
make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name, Trait.SEQUENCE)
default()
@staticmethod
def sequence_insert_fp8x23():
def default():
sequence = []
tensor_cnt = 3
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
val = np.random.randint(0, 6, shape).astype(np.float64)
t = Tensor(Dtype.FP8x23, val.shape, to_fp(
val.flatten(), FixedImpl.FP8x23))
sequence.append(t)
val = np.random.randint(0, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, val.shape, to_fp(
val.flatten(), FixedImpl.FP8x23))
position = np.random.randint(-2, 2)
expected_sequence = sequence.copy()
expected_sequence.insert(position, tensor)
name = "sequence_insert_fp8x23"
make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name, Trait.SEQUENCE)
default()
@staticmethod
def sequence_insert_fp16x16():
def default():
sequence = []
tensor_cnt = 3
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
val = np.random.randint(0, 6, shape).astype(np.float64)
t = Tensor(Dtype.FP16x16, val.shape, to_fp(
val.flatten(), FixedImpl.FP16x16))
sequence.append(t)
val = np.random.randint(0, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, val.shape, to_fp(
val.flatten(), FixedImpl.FP16x16))
position = np.random.randint(-2, 2)
expected_sequence = sequence.copy()
expected_sequence.insert(position, tensor)
name = "sequence_insert_fp16x16"
make_test([sequence, tensor, scalar(position)], expected_sequence, "input_0.sequence_insert(@input_1,Option::Some(input_2))", name, Trait.SEQUENCE)
default()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/sequence_length.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
scalar = lambda x: Tensor(Dtype.U32, (), np.array([x]).astype(np.uint32).flatten())
class Sequence_length(RunAll):
@staticmethod
def sequence_length_u32():
def default():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
name = "sequence_length_u32"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
def broadcast():
sequence = []
tensor_cnt = np.random.randint(1, 10)
for _ in range(tensor_cnt):
shape = np.random.randint(1, 4, 2)
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dtype.U32, values.shape, values.flatten())
sequence.append(tensor)
name = "sequence_length_u32_broadcast"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
default()
broadcast()
@staticmethod
def sequence_length_i32():
def default():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
name = "sequence_length_i32"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
def broadcast():
sequence = []
tensor_cnt = np.random.randint(1, 10)
for _ in range(tensor_cnt):
shape = np.random.randint(1, 4, 2)
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
name = "sequence_length_i32_broadcast"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
default()
broadcast()
@staticmethod
def sequence_length_i8():
def default():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
name = "sequence_length_i8"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
def broadcast():
sequence = []
tensor_cnt = np.random.randint(1, 10)
for _ in range(tensor_cnt):
shape = np.random.randint(1, 4, 2)
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
sequence.append(tensor)
name = "sequence_length_i8_broadcast"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
default()
broadcast()
@staticmethod
def sequence_length_fp8x23():
def default():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
name = "sequence_length_fp8x23"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
def broadcast():
sequence = []
tensor_cnt = np.random.randint(1, 10)
for _ in range(tensor_cnt):
shape = np.random.randint(1, 4, 2)
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.append(tensor)
name = "sequence_length_fp8x23_broadcast"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
default()
broadcast()
@staticmethod
def sequence_length_fp16x16():
def default():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
name = "sequence_length_fp16x16"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
def broadcast():
sequence = []
tensor_cnt = np.random.randint(1, 10)
for _ in range(tensor_cnt):
shape = np.random.randint(1, 4, 2)
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(values.flatten(), FixedImpl.FP16x16))
sequence.append(tensor)
name = "sequence_length_fp16x16_broadcast"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
default()
broadcast()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/shrink.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
def shrink(input_array: np.ndarray, bias: float, lambd: float) -> np.ndarray:
output_array = np.where(input_array > lambd, input_array - bias,
np.where(input_array < -lambd, input_array + bias, 0))
return output_array
class Shrink(RunAll):
@staticmethod
def shrink_fp8x23():
def shrink_hard():
x = np.random.uniform(-3, 3, (3, 3, 3)).astype(np.float64)
bias = np.float64(0) # Default value
lambd = np.float64(1)
y = shrink(x, bias, lambd)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "shrink_hard_fp8x23"
make_test([x], y, "TensorTrait::shrink(input_0, Option::None(()), Option::Some(FixedTrait::new(8388608, false)))", name)
def shrink_soft():
x = np.random.uniform(-3, 3, (3, 3, 3)).astype(np.float64)
bias = np.float64(1)
lambd = np.float64(1)
y = shrink(x, bias, lambd)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "shrink_soft_fp8x23"
make_test([x], y, "TensorTrait::shrink(input_0, Option::Some(FixedTrait::new(8388608, false)), Option::Some(FixedTrait::new(8388608, false)))", name)
shrink_hard()
shrink_soft()
@staticmethod
def shrink_fp16x16():
def shrink_hard():
x = np.random.uniform(-3, 3, (3, 3, 3)).astype(np.float64)
bias = np.float64(0) # Default value
lambd = np.float64(1)
y = shrink(x, bias, lambd)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "shrink_hard_fp16x16"
make_test([x], y, "TensorTrait::shrink(input_0, Option::None(()), Option::Some(FixedTrait::new(65536, false)))", name)
def shrink_soft():
x = np.random.uniform(-3, 3, (3, 3, 3)).astype(np.float64)
bias = np.float64(1)
lambd = np.float64(1)
y = shrink(x, bias, lambd)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "shrink_soft_fp16x16"
make_test([x], y, "TensorTrait::shrink(input_0, Option::Some(FixedTrait::new(65536, false)), Option::Some(FixedTrait::new(65536, false)))", name)
shrink_hard()
shrink_soft()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/sigmoid.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
import tensorflow as tf
class Sigmoid(RunAll):
@staticmethod
def fp8x23():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float32)
y = tf.keras.activations.sigmoid(x).numpy()
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "sigmoid_fp8x23"
make_test([x], y, "NNTrait::sigmoid(@input_0)",
name, Trait.NN)
@staticmethod
def fp16x16():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float32)
y = tf.keras.activations.sigmoid(x).numpy()
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "sigmoid_fp16x16"
make_test([x], y, "NNTrait::sigmoid(@input_0)",
name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/sign.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Sign(RunAll):
@staticmethod
def sign_i8():
def sign():
x = np.array(range(-5, 6)).astype(np.int8)
y = np.array([-1, -1, -1, -1, -1, 0, 1, 1, 1, 1, 1]).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "sign_i8"
make_test(
[x], y, "input_0.sign()", name)
sign()
@staticmethod
def sign_i32():
def sign():
x = np.array(range(-5, 6)).astype(np.int32)
y = np.array([-1, -1, -1, -1, -1, 0, 1, 1, 1, 1, 1]).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "sign_i32"
make_test(
[x], y, "input_0.sign()", name)
sign()
@staticmethod
def sign_fail():
def sign():
x = np.array(range(-5, 6)).astype(np.int32)
y = np.array([1, -1, -1, -1, -1, 0, 1, 1, 1, 1, -1]).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "sign_fail"
make_test(
[x], y, "input_0.sign()", name)
sign()
@staticmethod
def sign_fP16x16():
def sign():
x = to_fp (np.array(range(-5, 6)).astype(np.int64), FixedImpl.FP16x16)
y = to_fp (np.array([-1, -1, -1, -1, -1, 0, 1, 1, 1, 1, 1]).astype(np.int64), FixedImpl.FP16x16)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "sign_fP16x16"
make_test(
[x], y, "input_0.sign()", name)
sign()
@staticmethod
def sign_fP8x23():
def sign():
x = to_fp (np.array(range(-5, 6)).astype(np.int64), FixedImpl.FP8x23)
y = to_fp (np.array([-1, -1, -1, -1, -1, 0, 1, 1, 1, 1, 1]).astype(np.int64), FixedImpl.FP8x23)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "sign_fP8x23"
make_test(
[x], y, "input_0.sign()", name)
sign()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/sin.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Sin(RunAll):
@staticmethod
def sin_fp8x23():
x = np.random.uniform(-3, 7, (2, 2)).astype(np.float64)
y = np.sin(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "sin_fp8x23"
make_test([x], y, "input_0.sin()", name)
@staticmethod
def sin_fp16x16():
x = np.random.uniform(-3, 7, (2, 2)).astype(np.float64)
y = np.sin(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "sin_fp16x16"
make_test([x], y, "input_0.sin()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/sinh.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Sinh(RunAll):
@staticmethod
def sinh_fp8x23():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = np.sinh(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "sinh_fp8x23"
make_test([x], y, "input_0.sinh()", name)
@staticmethod
def sinh_fp16x16():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = np.sinh(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "sinh_fp16x16"
make_test([x], y, "input_0.sinh()", name)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/slice.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Slice(RunAll):
@staticmethod
def slice_u32():
def slice_2D():
x = np.random.randint(0, 255, (2, 4)).astype(np.uint32)
y = x[0:2, 2:4]
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "slice_u32_2d"
make_test(
[x], y, "input_0.slice(array![0, 2].span(), array![2, 4].span(), Option::Some(array![0, 1].span()), Option::Some(array![1, 1].span()))", name)
def slice_3D():
x = np.random.randint(0, 255, (20, 10, 5)).astype(np.uint32)
y = x[0:3, 0:10:3]
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "slice_u32_3d"
make_test(
[x], y, "input_0.slice(array![0, 0].span(), array![3, 10].span(), Option::Some(array![0, 1].span()), Option::Some(array![1, 3].span()))", name)
slice_2D()
slice_3D()
@staticmethod
def slice_i32():
def slice_2D():
x = np.random.randint(-127, 127, (2, 4)).astype(np.int32)
y = x[0:2, 2:4]
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "slice_i32_2d"
make_test(
[x], y, "input_0.slice(array![0, 2].span(), array![2, 4].span(), Option::Some(array![0, 1].span()), Option::Some(array![1, 1].span()))", name)
def slice_3D():
x = np.random.randint(-127, 127, (20, 10, 5)).astype(np.int32)
y = x[0:3, 0:10:3]
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "slice_i32_3d"
make_test(
[x], y, "input_0.slice(array![0, 0].span(), array![3, 10].span(), Option::Some(array![0, 1].span()), Option::Some(array![1, 3].span()))", name)
slice_2D()
slice_3D()
@staticmethod
def slice_i8():
def slice_2D():
x = np.random.randint(-127, 127, (2, 4)).astype(np.int8)
y = x[0:2, 2:4]
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "slice_i8_2d"
make_test(
[x], y, "input_0.slice(array![0, 2].span(), array![2, 4].span(), Option::Some(array![0, 1].span()), Option::Some(array![1, 1].span()))", name)
def slice_3D():
x = np.random.randint(-127, 127, (20, 10, 5)).astype(np.int8)
y = x[0:3, 0:10:3]
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "slice_i8_3d"
make_test(
[x], y, "input_0.slice(array![0, 0].span(), array![3, 10].span(), Option::Some(array![0, 1].span()), Option::Some(array![1, 3].span()))", name)
slice_2D()
slice_3D()
@staticmethod
def slice_fp8x23():
def slice_2D():
x = to_fp(np.random.randint(-127, 127, (2, 4)
).astype(np.int64), FixedImpl.FP8x23)
y = x[0:2, 2:4]
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "slice_fp8x23_2d"
make_test(
[x], y, "input_0.slice(array![0, 2].span(), array![2, 4].span(), Option::Some(array![0, 1].span()), Option::Some(array![1, 1].span()))", name)
def slice_3D():
x = to_fp(np.random.randint(-127, 127, (20, 10, 5)
).astype(np.int64), FixedImpl.FP8x23)
y = x[0:3, 0:10:3]
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "slice_fp8x23_3d"
make_test(
[x], y, "input_0.slice(array![0, 0].span(), array![3, 10].span(), Option::Some(array![0, 1].span()), Option::Some(array![1, 3].span()))", name)
slice_2D()
slice_3D()
@staticmethod
def slice_fp16x16():
def slice_2D():
x = to_fp(np.random.randint(-127, 127, (2, 4)
).astype(np.int64), FixedImpl.FP16x16)
y = x[0:2, 2:4]
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "slice_fp16x16_2d"
make_test(
[x], y, "input_0.slice(array![0, 2].span(), array![2, 4].span(), Option::Some(array![0, 1].span()), Option::Some(array![1, 1].span()))", name)
def slice_3D():
x = to_fp(np.random.randint(-127, 127, (20, 10, 5)
).astype(np.int64), FixedImpl.FP16x16)
y = x[0:3, 0:10:3]
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "slice_fp16x16_3d"
make_test(
[x], y, "input_0.slice(array![0, 0].span(), array![3, 10].span(), Option::Some(array![0, 1].span()), Option::Some(array![1, 3].span()))", name)
slice_2D()
slice_3D()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/softmax.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
x_max = np.max(x, axis=axis, keepdims=True)
tmp = np.exp(x - x_max)
s = np.sum(tmp, axis=axis, keepdims=True)
return tmp / s
class Softmax(RunAll):
@staticmethod
def axis_0():
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
y = softmax(x, axis=0)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "softmax_axis_0"
make_test([x], y, "NNTrait::softmax(@input_0, Option::Some(0))",
name, Trait.NN)
@staticmethod
def axis_1():
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
y = softmax(x, axis=1)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "softmax_axis_1"
make_test([x], y, "NNTrait::softmax(@input_0, Option::Some(1))",
name, Trait.NN)
@staticmethod
def axis_2():
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
y = softmax(x, axis=2)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "softmax_axis_2"
make_test([x], y, "NNTrait::softmax(@input_0, Option::Some(2))",
name, Trait.NN)
@staticmethod
def axis_minus_1():
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
y = softmax(x, axis=-1)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "softmax_axis_minus_1"
make_test([x], y, "NNTrait::softmax(@input_0, Option::None)",
name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/softmax_zero.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def softmax_zero(x: np.ndarray, axis: int = -1) -> np.ndarray:
x_max = np.max(x, axis=axis, keepdims=True)
tmp = np.exp(x - x_max)
tmp = np.where(x == 0.0, 0.0, tmp)
s = np.sum(tmp, axis=axis, keepdims=True)
s = np.where(s == 0.0, 1, s)
return tmp / s
class Softmax_zero(RunAll):
@staticmethod
def fp8x23():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = softmax_zero(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "softmax_zero_fp8x23"
make_test([x], y, "NNTrait::softmax_zero(@input_0, 1)",
name, Trait.NN)
@staticmethod
def fp16x16():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = softmax_zero(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "softmax_zero_fp16x16"
make_test([x], y, "NNTrait::softmax_zero(@input_0, 1)",
name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/softplus.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def softplus(x: np.ndarray) -> np.ndarray:
return np.log(np.exp(x) + 1)
class Softplus(RunAll):
@staticmethod
def softplus_fp():
def fp8x23():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = softplus(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "softplus_fp8x23"
make_test([x], y, "NNTrait::softplus(@input_0)",
name, Trait.NN)
def fp16x16():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = softplus(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "softplus_fp16x16"
make_test([x], y, "NNTrait::softplus(@input_0)",
name, Trait.NN)
fp8x23()
fp16x16()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/softsign.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def softsign(x: np.ndarray) -> np.ndarray:
return x / (1 + np.abs(x))
class Softsign(RunAll):
@staticmethod
def softsign_fp():
def fp8x23():
x = np.random.uniform(-5, 7, (2, 2)).astype(np.float64)
y = softsign(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "softsign_fp8x23"
make_test([x], y, "NNTrait::softsign(@input_0)",
name, Trait.NN)
def fp16x16():
x = np.random.uniform(-5, 7, (2, 2)).astype(np.float64)
y = softsign(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "softsign_fp16x16"
make_test([x], y, "NNTrait::softsign(@input_0)",
name, Trait.NN)
fp8x23()
fp16x16()
|
https://github.com/gizatechxyz/orion
|
nodegen/node/space_to_depth.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def space_to_depth(data: np.ndarray, blocksize: int = 2) -> np.ndarray:
if len(data.shape) != 4:
raise RuntimeError(f"Unexpected shape {data.shape!r}.")
b, C, H, W = data.shape
tmpshape = (
b,
C,
H // blocksize,
blocksize,
W // blocksize,
blocksize,
)
reshaped = np.reshape(data, tmpshape)
transposed = np.transpose(reshaped, [0, 3, 5, 1, 2, 4])
finalshape = (
b,
C * blocksize * blocksize,
H // blocksize,
W // blocksize,
)
y = np.reshape(transposed, finalshape).astype(data.dtype)
return y
class Space_to_depth(RunAll):
@staticmethod
def fp8x23():
x = np.random.uniform(-3, 3, (1, 2, 2, 4)).astype(np.float64)
y = space_to_depth(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "space_to_depth_fp8x23"
make_test([x], y, "NNTrait::space_to_depth(@input_0, 2)",
name, Trait.NN)
@staticmethod
def fp16x16():
x = np.random.uniform(-3, 3, (1, 2, 2, 4)).astype(np.float16)
y = space_to_depth(x)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "space_to_depth_fp16x16"
make_test([x], y, "NNTrait::space_to_depth(@input_0, 2)",
name, Trait.NN)
# @staticmethod
# def fp64x64():
# x = np.random.uniform(-3, 3, (1, 2, 2, 4)).astype(np.float64)
# y = space_to_depth(x)
# x = Tensor(Dtype.FP64x64, x.shape, to_fp(
# x.flatten(), FixedImpl.FP64x64))
# y = Tensor(Dtype.FP64x64, y.shape, to_fp(
# y.flatten(), FixedImpl.FP64x64))
# name = "space_to_depth_fp64x64"
# make_test([x], y, "NNTrait::space_to_depth(@input_0, 2)",
# name, Trait.NN)
@staticmethod
def fpi8():
x = np.random.randint(-3, 3, (1, 2, 2, 4)).astype(np.int8)
y = space_to_depth(x)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "space_to_depth_i8"
make_test([x], y, "NNTrait::space_to_depth(@input_0, 2)",
name, Trait.NN)
@staticmethod
def fpi32():
x = np.random.randint(-3, 3, (1, 2, 2, 4)).astype(np.int32)
y = space_to_depth(x)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "space_to_depth_i32"
make_test([x], y, "NNTrait::space_to_depth(@input_0, 2)",
name, Trait.NN)
@staticmethod
def fpu32():
x = np.random.randint(-3, 3, (1, 2, 2, 4)).astype(np.uint32)
y = space_to_depth(x)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "space_to_depth_u32"
make_test([x], y, "NNTrait::space_to_depth(@input_0, 2)",
name, Trait.NN)
|
https://github.com/gizatechxyz/orion
|
nodegen/node/split.py
|
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Split(RunAll):
@staticmethod
def split_u32():
def split_1D():
x = np.random.randint(0, 255, 6).astype(np.uint32)
y = [
np.array(x[0:2]).astype(np.uint32),
np.array(x[2:4]).astype(np.uint32),
np.array(x[4:6]).astype(np.uint32),
]
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = [
Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
Tensor(Dtype.U32, y[2].shape, y[2].flatten()),
]
name = "split_u32_1d_equal_parts"
make_test(
[_x], _y, "input_0.split(0, Option::Some(3), Option::None(()))", name)
y = [
np.array(x[0:2]).astype(np.uint32),
np.array(x[2:6]).astype(np.uint32),
]
_y = [
Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
]
name = "split_u32_1d_variable_parts"
make_test(
[_x], _y, "input_0.split(0, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)))", name)
def split_2D():
x = np.random.randint(0, 255, (2, 6)).astype(np.uint32)
y = [
np.array(x[0:2, 0:3]).astype(np.uint32),
np.array(x[0:2, 3:6]).astype(np.uint32),
]
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = [
Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
]
name = "split_u32_2d_equal_parts"
make_test(
[_x], _y, "input_0.split(1, Option::Some(2), Option::None(()))", name)
y = [
np.array(x[0:2, 0:2]).astype(np.uint32),
np.array(x[0:2, 2:6]).astype(np.uint32)
]
_y = [
Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
]
name = "split_u32_2d_variable_parts"
make_test(
[_x], _y, "input_0.split(1, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)))", name)
def split_zero_size():
# 1-dimensional tensor with dimension_size=0
x = np.array([]).astype(np.uint32)
y = [
np.array([]).astype(np.uint32),
np.array([]).astype(np.uint32),
np.array([]).astype(np.uint32),
]
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = [
Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
Tensor(Dtype.U32, y[2].shape, y[2].flatten()),
]
# Split emtpy tensor to tensors of size zero
name = "split_u32_zero_size"
make_test(
[_x], _y, "input_0.split(0, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![3].span(), data: array![0, 0, 0].span(),)))", name)
def split_1d_uneven():
x = np.random.randint(0, 255, 7).astype(np.uint32)
y = [
np.array(x[0:2]).astype(np.uint32),
np.array(x[2:4]).astype(np.uint32),
np.array(x[4:6]).astype(np.uint32),
np.array(x[6:7]).astype(np.uint32),
]
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = [
Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
Tensor(Dtype.U32, y[2].shape, y[2].flatten()),
Tensor(Dtype.U32, y[3].shape, y[3].flatten()),
]
name = "split_u32_1d_uneven"
make_test(
[_x], _y, "input_0.split(0, Option::Some(4), Option::None(()))", name)
def split_2d_uneven():
x = np.random.randint(0, 255, (2, 8)).astype(np.uint32)
y = [
np.array(x[0:2, 0:3]).astype(np.uint32),
np.array(x[0:2, 3:6]).astype(np.uint32),
np.array(x[0:2, 6:8]).astype(np.uint32)
]
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = [
Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
Tensor(Dtype.U32, y[2].shape, y[2].flatten()),
]
name = "split_u32_2d_uneven"
make_test(
[_x], _y, "input_0.split(1, Option::Some(3), Option::None(()))", name)
split_1D()
split_2D()
split_zero_size()
split_1d_uneven()
split_2d_uneven()
@staticmethod
def split_fp16x16():
def split_1D():
x = to_fp(np.random.randint(-127, 127, 6
).astype(np.int64), FixedImpl.FP16x16)
y = [
np.array(x[0:2]).astype(np.int64),
np.array(x[2:4]).astype(np.int64),
np.array(x[4:6]).astype(np.int64),
]
_x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
_y = [
Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
Tensor(Dtype.FP16x16, y[2].shape, y[2].flatten()),
]
name = "split_fp16x16_1d_equal_parts"
make_test(
[_x], _y, "input_0.split(0, Option::Some(3), Option::None(()))", name)
y = [
np.array(x[0:2]).astype(np.int64),
np.array(x[2:6]).astype(np.int64),
]
_y = [
Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
]
name = "split_fp16x16_1d_variable_parts"
make_test(
[_x], _y, "input_0.split(0, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)))", name)
def split_2D():
x = to_fp(np.random.randint(-127, 127, (2, 6)
).astype(np.int64), FixedImpl.FP16x16)
y = [
np.array(x[0:2, 0:3]).astype(np.int64),
np.array(x[0:2, 3:6]).astype(np.int64),
]
_x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
_y = [
Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
]
name = "split_fp16x16_2d_equal_parts"
make_test(
[_x], _y, "input_0.split(1, Option::Some(2), Option::None(()))", name)
y = [
np.array(x[0:2, 0:2]).astype(np.int64),
np.array(x[0:2, 2:6]).astype(np.int64)
]
_y = [
Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
]
name = "split_fp16x16_2d_variable_parts"
make_test(
[_x], _y, "input_0.split(1, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)))", name)
def split_zero_size():
# 1-dimensional tensor with dimension_size=0
x = to_fp(np.array([]).astype(np.int64
).astype(np.int64), FixedImpl.FP16x16)
y = [
np.array([]).astype(np.int64),
np.array([]).astype(np.int64),
np.array([]).astype(np.int64),
]
_x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
_y = [
Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
Tensor(Dtype.FP16x16, y[2].shape, y[2].flatten()),
]
# Split emtpy tensor to tensors of size zero
name = "split_fp16x16_zero_size"
make_test(
[_x], _y, "input_0.split(0, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![3].span(), data: array![0, 0, 0].span(),)))", name)
def split_1d_uneven():
x = to_fp(np.random.randint(-127, 127, 7
).astype(np.int64), FixedImpl.FP16x16)
y = [
np.array(x[0:2]).astype(np.int64),
np.array(x[2:4]).astype(np.int64),
np.array(x[4:6]).astype(np.int64),
np.array(x[6:7]).astype(np.int64),
]
_x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
_y = [
Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
Tensor(Dtype.FP16x16, y[2].shape, y[2].flatten()),
Tensor(Dtype.FP16x16, y[3].shape, y[3].flatten()),
]
name = "split_fp16x16_1d_uneven"
make_test(
[_x], _y, "input_0.split(0, Option::Some(4), Option::None(()))", name)
def split_2d_uneven():
x = to_fp(np.random.randint(-127, 127, (2, 8)
).astype(np.int64), FixedImpl.FP16x16)
y = [
np.array(x[0:2, 0:3]).astype(np.int64),
np.array(x[0:2, 3:6]).astype(np.int64),
np.array(x[0:2, 6:8]).astype(np.int64)
]
_x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
_y = [
Tensor(Dtype.FP16x16, y[0].shape, y[0].flatten()),
Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
Tensor(Dtype.FP16x16, y[2].shape, y[2].flatten()),
]
name = "split_fp16x16_2d_uneven"
make_test(
[_x], _y, "input_0.split(1, Option::Some(3), Option::None(()))", name)
split_1D()
split_2D()
split_zero_size()
split_1d_uneven()
split_2d_uneven()
|
https://github.com/gizatechxyz/orion
|
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